Economic Assumptions Used in the Agencies’ Analyses

1 

### Table of Contents

CHAPTER 4: ECONOMIC ASSUMPTIONS USED IN THE AGENCIES’ ANALYSES

2 4

- 2

4.1 How the Agencies Use the Economic Assumptions in th eir Analyses ....................... 2 4

- 2

4.2 What Economic Assumptions Do the Agencies Use? ....

.............................................. 3 4

- 3

4.2.1 Potential Opportunity Costs of Improved Fuel Econom y and Reduced GHG Emissions .........................................

................................................... .................................. 3 4

- 3

4.2.2 The On-Road Fuel Economy “Gap” ....................

......................................... 44

- 3 4

- 4

4.2.3 Fuel Prices and the Value of Saving Fuel ..........

........................................... 64

- 3 4

- 6

4.2.4 Vehicle Survival and Use Assumptions ..............

........................................... 84

- 3 4

- 8

4.2.5 Accounting for the Fuel Economy Rebound Effect ....

............................. 264

- 3 4

- 17

4.2.6 Benefits from Increased Vehicle Use ...............

.......................................... 334

- 3 4

- 22

4.2.7 Added Costs from Increased Vehicle Use ............

..................................... 334

- 3 4

- 23

4.2.8 Petroleum and Energy Security Impacts .............

..................................... 344

- 3 4

- 24

4.2.9 Air Pollutant Emissions ...........................

................................................... 444

- 3 4

- 33

4.2.10 Reductions in Emissions of Greenhouse Gases .......

................................. 544

- 3 4

- 43

4.2.11 The Value of Increased Driving Range ..............

....................................... 554

- 3 4

- 44

4.2.12 Discounting Future Benefits and Costs .............

........................................ 584

- 3 4

- 45 Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Field Code ChangedDraft Joint Technical Support Document

2



## CHAPTER 4: Economic Assumptions Used in the Agencies’ Analyses

4.1 How the Agencies Use the Economic Assumptions in th eir Analyses

Improving new vehicles’ fuel efficiency and reducin g greenhouse gas (GHG) emissions provides direct benefits to their buyers and users by reducing fuel consumption and fuel costs throughout those vehicles’ lifetimes, stimulating i ncreased vehicle use through the fuel economy rebound effect, and increasing vehicles’ driving ra nge so that they require less frequent refueling. At the same time, the reduction in fuel use that re sults from requiring higher fuel economy and reducing GHGs also produces wider benefits to the U .S. economy by lowering the cost of economic externalities that result from U.S. petrol eum consumption and imports, including reducing the price of petroleum, lowering the poten tial costs from disruption in the flow of oil imports, and possibly reducing federal outlays to s ecure imported oil supplies and cushion the U.S. economy against their potential interruption. Reducing fuel consumption and GHGs also lowers the economic costs of environmental external ities resulting from fuel production and use, including reducing the impacts on human health impa cts from emissions of criteria air pollutants, and reducing future economic damages from potential changes in the global climate caused by greenhouse gas emissions.

These benefits are partly offset by the increase in fuel use that results from added vehicle use due to the fuel economy rebound effect, as well as by added costs from the increased congestion, crashes, and noise caused by increased vehicle use. They might also be offset by any loss in the utility that new vehicles provide to th eir buyers (and subsequent owners) as a consequence of reductions in their performance, car rying capacity, or comfort that manufacturers may implement as part of their strategies to comply with higher fuel economy requirements and GHG standards. Nevertheless, the total economic be nefits from requiring higher fuel economy and reducing GHGs are likely to be substantial, and the EPA and NHTSA have attempted to develop detailed estimates of the economic benefits from adopting more stringent standards.

This section discusses the common economic values u sed by both NHTSA and EPA. These economic inputs incorporate a range of foreca st information, economic estimates, and input parameters. This section describes the sourc es that EPA and NHTSA have relied upon for this information, the rationale underlying each ass umption, and the agencies’ proposed estimates of specific parameter values. These common economi c values are then used as inputs into each Agencies’ respective modeling and other analyses of the economic benefits and costs of the EPA and NHTSA programs. While the underlying economic input values are common to both Agencies, it is the differences in the way each Age ncy assesses its program, that result in differing proposed benefits estimates. This issue is discussed further in Section 1.C of the preamble to the joint rulemaking.Economic Assumptions Used in the Agencies’ Analyses

3 

### 4.2 What Economic Assumptions Do the Agencies Use?

4.2.1 Potential Opportunity Costs of Improved Fuel Econom y and Reduced GHG Emissions

An important concern is whether achieving the fuel economy improvements required by alternative fuel economy or CO 2 emissions standards would require manufacturers to compromise the performance, carrying capacity, safe ty, or comfort of their vehicle models. If it did so, the resulting sacrifice in the value of the se attributes to consumers would represent an additional cost of achieving the required improveme nts in fuel economy, and thus of manufacturers’ compliance with stricter standards. Although the exact values that vehicle buyers attach to vehicle attributes such as fuel economy, performance, passenger- and cargo-carrying capacity, and other dimensions of vehicle utility a re difficult to infer from their purchasing decisions and vehicle prices, changing vehicle attr ibutes can significantly affect the overall utility that vehicles provide to their owners, and thus their value to potential buyers.

EPA and NHTSA have approached this potential proble m by developing proposed cost estimates for fuel economy-improving technologies t hat include any additional manufacturing costs that would be necessary to maintain the refer ence fleet levels of performance, comfort, capacity, or safety of any light-duty vehicle model to which those technologies are applied. In doing so, the agencies primarily followed the prece dent established by the 2002 NAS Report, although EPA and NHTSA have extensively updated the ir assumptions as necessary for the purposes of the current rulemaking. The NAS study estimated “constant performance and utility” costs for fuel economy technologies, and E PA and NHTSA have used these as the basis for their continuing efforts to refine the technolo gy costs they employ in analyzing manufacturer’s costs complying with alternative pas senger car and light truck fuel efficiency and GHG standards for MYs 2012-2016. Although the agen cies have revised its estimates of manufacturers’ costs for some technologies signific antly for use in this rulemaking, these revised estimates are still intended to represent costs tha t would allow manufacturers to maintain the performance, carrying capacity, and utility of vehi cle models while improving their fuel
economy.
EPA and NHTSA acknowledge the difficulty of estimat ing technology costs that include costs for the accompanying changes in vehicle desig n that are necessary to maintain performance, capacity, and utility. We believe tha t our proposed cost estimates for fuel economy-improving technologies should be generally sufficient to prevent significant reductions in consumer welfare provided by vehicle models to w hich manufacturers apply those technologies. However, it is possible that the agen cies’ technology cost estimates do not include adequate allowance for the necessary efforts by man ufacturers to maintain vehicle performance, carrying capacity, and utility while improving fuel economy. If this is the case, the true economic costs of achieving higher fuel economy wou ld include the opportunity costs to vehicle owners of any sacrifices in vehicles’ performance, carrying capacity, and utility that accompanied increases in their fuel economy. In th at event, the agencies’ estimated technology costs would underestimate the true economic costs o f achieving it.

Recognizing this possibility, EPA and NHTSA may con sider in the future whether there are credible methods available to estimate explicit ly the changes in vehicle buyers’ welfare fromDraft Joint Technical Support Document

4

the combination of higher prices for new vehicle mo dels, increases in their fuel economy, and any accompanying changes in vehicle attributes such as performance, passenger- and cargocarrying capacity, or other dimensions of utility. The net change in buyer’s welfare that results from the combination of these changes would provide an estimate of the true economic costs for improving fuel economy, which would fall on vehicle purchasers and subsequent owners rather than on manufacturers. As EPA discusses in its DRI A in Section 8.1.2, though, EPA believes that the state of the art for developing these esti mates may not be sufficient to provide credible estimates as indicated by wide variation in modelin g methods, few assessments of the accuracy or consistency of results, and inconsistent results when comparisons have been made. NHTSA, in its PRIA in Section XX, also discusses its diffi culties with implementing vehicle choice models that, among other features, could estimate c hanges in consumer welfare. Although EPA and NHTSA have continued to rely on estimates of wh at it believes are “constant performance and utility” technology costs for this proposed an alysis of the costs of complying with alternative fuel efficiency standards for MY 20120 -2016 cars and light trucks, the agencies seek comment on alternative ways to deal with this impor tant issue.



### 4.2.2 The On-Road Fuel Economy “Gap”

Actual fuel economy levels achieved by vehicles in on-road driving fall significantly short of their levels measured under the laboratory -like test conditions used under the CAFE program to establish its published fuel economy rat ings for different models. In analyzing the fuel savings from passenger car and light truck fue l efficiency and GHG standards, the agency adjusts the actual fuel economy performance of each passenger car and light truck model downward from its rated value to reflect the expect ed size of this on-road fuel economy “gap.” In December 2006, EPA adopted changes to its regula tions on fuel economy labeling, which were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel economy levels. 1

Comparisons of on-road and CAFE fuel economy levels developed by EPA as part of its Final Rule implementing new fuel economy labeling r equirements for new vehicles indicate that actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published fuel economy levels. 2 For example, if the overall EPA fuel economy rati ng of a light truck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicle is expected to be 16 mpg (20*.80). In its analysis supporting the Final Rule establishing CAFE standards for MY 2011, NHTSA employed EPA’s revised estimate of t his on-road fuel economy gap in its analysis of the fuel savings resulting from alterna tive fuel efficiency standards.

An analysis conducted by NHTSA confirmed that EPA’s estimate of a 20 percent gap between test and on-road fuel economy is well-found ed. The agency used data on the number of passenger cars and light trucks of each model year that were in service (registered for use) during each calendar year from 2000 through 2006, average fuel economy for passenger cars and light trucks produced during each model year, and estimat es of average miles driven per year by cars and light trucks of different ages during each cale ndar year over that period. These data were combined to develop estimates of the usage-weighted average fuel economy that the U.S. passenger car and light truck fleets would have ach ieved during each year from 2000 through 2006 under test conditions.Economic Assumptions Used in the Agencies’ Analyses

5 Table 4-1Table 4

- 1 Table 4

- 1 compares NHTSA’s estimates of fleet-wide average f uel economy under test conditions for 2000 through 2006 to the Federal Highway Administration’s (FHWA) published estimates of actual on-road fuel e conomy achieved by passenger cars and light trucks during each of those years. As it sho ws, FHWA’s estimates of actual fuel economy for passenger cars ranged from 21 percent to 23 per cent lower than NHTSA’s estimates of its fleet-wide average value under test conditions over this period. Similarly, FHWA’s estimates of actual fuel economy for light trucks ranged from 16 percent to 18 percent lower than NHTSA’s estimates of average light truck fuel economy under test conditions. Thus, these results appear to confirm that the 20 percent on-road fuel economy di scount or gap represents a reasonable estimate for use in evaluating the fuel savings lik ely to result from alternative fuel efficiency standards for MY 2012-2016 vehicles.

We are aware of two potential issues involved in th ese estimates. One, the estimates of total annual car and truck VMT are developed by the states and submitted to FHWA. Each state uses its own definition of a car and a truck. For example, some states classify minivans as cars and some as trucks. Thus, there are known inconsis tencies with these estimates when evaluated separately for cars and trucks. Also, total gasoli ne consumption can be reasonably estimated from excise tax receipts, but separate estimates fo r cars and trucks are not available. We are not aware of the precise methodology used to develop th e distinct on-road fuel economy estimates for cars and trucks developed by FHWA. We do not b elieve that they are based on direct measurements from substantial numbers of vehicles, as no such test programs were found by EPA during its fuel economy labeling rule in 2006. Also, the year-to-year consistency for both car and truck fuel economy implies some methodology other than direct measurement. For this reason, NHTSA and EPA are not proposing to use dist inct on-road fuel economy gaps for cars and trucks, but one common value of 20% for both ve hicle classes for purposes of estimating the fuel savings of the proposed standards.

Table 4-1 Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks

Compared to Reported Fuel Economy

YEAR PASSENGER CARS LIGHT-DUTY TRUCKS

NHTSA Estimated Test MPG FHWA

Reported Actual MPG Percent

Difference NHTSA

Estimated Test MPG FHWA

Reported Actual MPG Percent

Difference

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3% 2001 28.2 22.1 -21.7% 20.8 17.6 -15.5% 2002 28.3 22.0 -22.3% 20.9 17.5 -16.2% 2003 28.4 22.2 -21.9% 21.0 17.2 -18.0% 2004 28.5 22.5 -21.1% 21.0 17.2 -18.3% 2005 28.6 22.1 -22.8% 21.1 17.7 -16.3%
2006 28.8 22.5 -21.8% 21.2 17.8 -16.2% Avg., 2000- 2006 28.4 22.2 -22.0% 21.0 17.5 -16.7% Field Code ChangedDraft Joint Technical Support Document

6



### 4.2.3 Fuel Prices and the Value of Saving Fuel

Projected future fuel prices are a critical input i nto the preliminary economic analysis of alternative fuel efficiency and GHG standards, beca use they determine the value of fuel savings both to new vehicle buyers and to society. EPA and NHTSA relied on the most recent fuel price projections from the U.S. Energy Information Admini stration’s (EIA) Annual Energy Outlook (AEO) for this analysis. Specifically, we used the AEO 2009 (April 2009 release) Reference Case forecasts of inflation-adjusted (constant-doll ar) retail gasoline and diesel fuel prices, which represent the EIA’s most up-to-date estimate of the most likely course of future prices for petroleum products. 3 While NHTSA relied on the forecasts of fuel price s presented in AEO 2008 High Price Case in the MY 2011 CAFE final rule , we noted at the time that we were relying on that estimate primarily because volatili ty in the oil market appeared to have overtaken the Reference Case, and that we anticipated that th e Reference Case forecast would be significantly higher in the next AEO. In fact, EIA ’s AEO 2009 Reference Case forecast projects higher retail fuel prices in most future years than those forecast in the High Price Case from AEO 2008. EPA and NHTSA are thus confident that th e AEO 2009 Reference Case is an appropriate forecast for projected future fuel pric es.
Federal government agencies generally use EIA’s pro jections in their assessments of future energy-related policies. The retail fuel pr ice forecasts presented in AEO 2009 span the period from 2009 through 2030. Measured in constan t 2007 dollars, the Reference Case forecast of retail gasoline prices during calendar year 2020 is $3.62 per gallon, rising gradually to $3.82 by the year 2030 (these values include federal, sta te and local taxes). However, valuing fuel savings over the maximum lifetimes of passenger car s and light trucks used in this analysis requires fuel price forecasts that extend through 2 050, approximately the last year during which a significant number of MY 2016 vehicles will remain in service. a To obtain fuel price forecasts for the years 2031 through 2050, the agency assumes that retail fuel prices will continue to increase after 2030 at the average annual rates pro jected for 2020-2030 in the AEO 2009 Revised Reference Case. b This assumption results in a projected retail pr ice of gasoline that reaches $4.25 in 2050.

The value of fuel savings resulting from improved f uel economy and GHG emissions to buyers of light-duty vehicles is determined by the retail price of fuel, which includes federal, state, and any local taxes imposed on fuel sales. Total taxes on gasoline, including federal, state, and local levies averaged $0.42 per gallon during 2 006, while those levied on diesel averaged $0.50. Because fuel taxes represent transfers of r esources from fuel buyers to government agencies, however, rather than real resources that are consumed in the process of supplying or

a The agency defines the maximum lifetime of vehicle s as the highest age at which more than 2 percent o f those originally produced during a model year remain in s ervice. In the case of light-duty trucks, for exam ple, this age has typically been 36 years for recent model years.

b This projection uses the rate of increase in fuel prices for 2020-2030 rather than that over the comp lete forecast period (2009-2030) because there is extreme volatil ity in the forecasts for the years 2009 through app roximately 2020. Using the average rate of change over the co mplete 2009-2030 forecast period would result in pr ojections of declining fuel prices after 2030.Economic Assumptions Used in the Agencies’ Analyses

7 using fuel, their value must be deducted from retai l fuel prices to determine the value of fuel savings resulting from more stringent fuel efficien cy and GHG standards to the U.S. economy as a whole.

In estimating the economy-wide or “social” value of fuel savings from improved fuel efficiency standards and GHG emission standards, EP A and NHTSA follow the assumptions used by EIA in AEO 2009 that state and local gasoli ne taxes will keep pace with inflation in nominal terms, and thus remain constant when expres sed in constant 2007 dollars. c In contrast, EIA assumes that federal gasoline taxes will remain unchanged in nominal terms, and thus decline throughout the forecast period when express ed in constant 2007 dollars. These differing assumptions about the likely future behavior of fed eral and state/local fuel taxes are consistent with recent historical experience, which reflects t he fact that federal as well as most state motor fuel taxes are specified on a cents-per-gallon basi s, and typically require legislation to change. The projected value of total taxes is deducted from each future year’s forecast of retail gasoline and diesel prices reported in AEO 2009 to determine the economic value of each gallon of fuel saved during that year as a result of improved fuel economy. Subtracting fuel taxes results in a projected value for saving gasoline of $3.22 per ga llon during 2020, rising to $3.45 per gallon by the year 2030.

EIA is widely-recognized as an impartial and author itative source of analysis and forecasts of U.S. energy production, consumption, a nd prices. The agency has published annual forecasts of energy prices and consumption levels f or the U.S. economy since 1982 in its AEOs. These forecasts have been widely relied upon by fed eral agencies for use in regulatory analysis and for other purposes. Since 1994, EIA’s annual f orecasts have been based upon the agency’s National Energy Modeling System (NEMS), which inclu des detailed representation of supply pathways, sources of demand, and their interaction to determine prices for different forms of
energy.
From 1982 through 1993, EIA’s forecasts of world oi l prices – the primary determinant of prices for gasoline, diesel, and other transport ation fuels derived from petroleum – consistently overestimated actual prices during fut ure years, often very significantly. Of the total of 119 forecasts of future world oil prices for the years 1985 through 2005 that EIA reported in its 1982-1993 editions of AEO, 109 overestimated th e subsequent actual values for those years, on average exceeding their corresponding actual val ues by 75 percent.

Since that time, however, EIA’s forecasts of future world oil prices show a more mixed record for accuracy. The 1994-2005 editions of AEO reported 91 separate forecasts of world oil prices for the years 1995-2005, of which 33 have su bsequently proven too high while the remaining 58 have underestimated actual prices. Th e average absolute error ( i.e. , regardless of

c By “social” value of fuel savings, the agencies me an that we value fuel savings at pre-tax prices, si nce this measures the value of the resources that are saved when less fuel is produced and consumed. The socia l value of saving fuel includes this plus the reductions in ec onomic and environmental externalities that result from consuming less fuel, importing less petroleum, and so on.Draft Joint Technical Support Document

8

its direction) of these forecasts has been 21 perce nt, but over- and underestimates have tended to offset one another, so that on average EIA’s more r ecent forecasts have underestimated actual world oil prices by 7 percent. Although both its o verestimates and underestimates of future world oil prices for recent years have often been l arge, the most recent editions of AEO have significantly underestimated petroleum prices durin g those years for which actual prices are now available.

However, EPA and NHTSA does not regard EIA’s recent tendency to underestimate future prices for petroleum and refined products or the high level of current fuel prices as adequate justification to employ forecasts that dif fer from the Reference Case forecast presented in the AEO 2009 Reference Case. This is particular ly the case because this forecast has been revised upward significantly since the initial rele ase of AEO 2008, which in turn represented a major upward revision from EIA’s fuel price forecas t reported previously in AEO 2007. Comparing different forecasts of world oil prices a lso shows that EIA’s Reference Case forecast reported in AEO 2009 is among the highest of all six publicly-available forecasts of world oi l prices over the 2010-30 time horizon. 4 Because world petroleum prices are the primary determinant of retail prices for refined petroleum products such as transportation fuels, this suggests that the Reference Case forecast of U.S. f uel prices reported in AEO 2009 is likely to be the highest of those projected by major forecasting services.



### 4.2.4 Vehicle Survival and Use Assumptions

The agencies’ analyses of fuel savings and related benefits from adopting higher fuel efficiency standards and establishing GHG emission standards for MY 2012-2016 passenger cars and light trucks is based on estimates of the resul ting changes in fuel use over their entire lifetimes in the U.S. vehicle fleet. The first ste p in estimating lifetime fuel consumption by vehicles produced during a model year is to calcula te the number of those vehicles that is expected to remain in service during each future ye ar after they are produced and sold. d This number is calculated by multiplying the number of v ehicles originally produced during a model year by the proportion expected to remain in servic e at the age they will have reached during each subsequent year, often referred to as a “survi val rate.”

In 2008, NHTSA updated its previous estimates of ca r and light truck survival rates using the most current registration data for vehicles pro duced during recent model years, in order to ensure that its forecasts of the number of these ve hicles remaining in use during future years reflect recent increases in the durability and expe cted life spans of cars and light trucks. 5 Both

d Vehicles are defined to be of age 1 during the cal endar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicle s are considered to be of age 1 during calendar yea r 2000, age 1 during calendar year 2001, and to reach their max imum age of 26 years during calendar year 2025. NHTSA considers the maximum lifetime of vehicles to be th e age after which less than 2 percent of the vehicl es originally produced during a model year remain in service. Ap plying these conventions to vehicle registration da ta indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 y ears. See Lu, S., NHTSA, Regulatory Analysis and Evaluation D ivision, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed July 27, 2009)

.Economic Assumptions Used in the Agencies’ Analyses

9 agencies used these updated survival estimates in t heir analyses of fuel savings and other benefits from adopting improved fuel efficiency and GHG stan dards for MY 2012-2106 cars and light trucks. The proportions of passenger cars and ligh t trucks expected to remain in service at each age up to their maximum lifetimes (26 and 36 years, respectively) are shown in Field Code ChangedDraft Joint Technical Support Document

10

Table 4-2Economic Assumptions Used in the Agencies’ Analyses

11 Table 4

- 2 Table 4

- 2 and Table 4-3. e

The next step in estimating fuel use is to calculat e the total number of miles that cars and light trucks produced in each model year affected b y the proposed fuel efficiency and GHG standards will be driven during each year of their lifetimes. To estimate total miles driven, the number of cars and light trucks projected to remain in use during each future year is multiplied by the average number of miles they are expected to be driven at the age they will have reached in that year.

Updated estimates of average annual miles driven by age were developed by NHTSA from the Federal Highway Administration’s 2001 Nati onal Household Transportation Survey, and these also differ from the estimates of annual mileage employed in past NHTSA analyses. f

e The maximum age of cars and light trucks was defin ed as the age when the number remaining in service has declined to approximately two percent of those orig inally produced. Based on an examination of recent registration data for previous model years, typical maximum ages appear to be 26 years for passenger cars and 36 ye ars for light trucks.

f See also NHTSA, “Vehicle Survival and Travel Mileag e Schedules,” Office of Regulatory Analysis and Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger ca rs and light trucks by age used in this analysis is the 2001 Nat ional Household Travel Survey (NHTS), jointly spons ored by the Federal Highway Administration, Bureau of Transport ation Statistics, and National Highway Traffic Safe ty Administration. Field Code Changed

Field Code ChangedDraft Joint Technical Support Document

12

Table 4-2Economic Assumptions Used in the Agencies’ Analyses

13 Table 4

- 2 Table 4

- 2 and Table 4-3 also report NHTSA’s updated estimat es of average car and light truck use. The total number of miles driven by passenger cars or light trucks produced during a model year, during each year of its lifeti me, is estimated by multiplying these agespecific estimates of average car and light truck u se by the number of vehicles projected to remain in service during that year.

As Field Code Changed

Field Code ChangedDraft Joint Technical Support Document

14

Table 4-2Economic Assumptions Used in the Agencies’ Analyses

15 Table 4

- 2 Table 4

- 2 and Table 4-3 also show, the resulting survival-we ighted mileage over the 26-year maximum lifetime of passenger cars is 161,8 47 miles, while that over the 36-year maximum lifetime of light trucks is 190,066 miles. Fuel savings and other benefits resulting from improved fuel efficiency and GHG standards for passenger cars and light trucks are calculated over their respective lifetimes and tota l expected mileage. It should be noted, however, that survival-weighted mileage is extremel y low (less than 1,000 miles per year) after age 20 for cars and after age 25 for light trucks, and thus has little impact on lifetime fuel savings or other benefits from higher fuel economy, particu larly after discounting those benefits to their present values.

The survival and annual mileage estimates reported in the tables reflect the convention that vehicles are defined to be of age 1 during the calendar year that coincides with their model year Thus for example, model year 2012 vehicles w ill be considered to be of age 1 during calendar year 2012. This convention is used in ord er to account for the fact that vehicles produced during a model year typical are first offe red for sale in June through September of the preceding calendar year (for example, sales of a mo del year typically begin in June through September of the previous calendar year, depending on manufacturer). Thus virtually all of the vehicles produced during a model year will be in us e for some or all of the calendar year coinciding with their model year, and they are cons idered to be of age 1 during that year. g

g As an illustration, virtually the entire productio n of model year 2012 cars and light trucks will hav e been sold by the end of calendar year 2012, so those vehicles ar e defined to be of age 1 during calendar year 2012. Model year 2012 vehicles are subsequently defined to be of age 2 during calendar year 2013, age 3 during calendar year 2014, and so on. One complication arises because registr ation data are typically collected for July 1 of ea ch calendar year, so not all vehicles produced during a model year wi ll appear in registration data until the calendar y ear when they have reached age 2 (and sometimes age 3) under this convention. Field Code ChangedDraft Joint Technical Support Document

16 Table 4-2 Survival Rates and Unadjusted Annual Miles Traveled (VMT)

by Age for Passenger Cars

Vehicle Age Estimated Survival

Fraction Estimated Annual VMT Survival-Weighted

Annual VMT
1 0.9950 14,231 14,160 2 0.9900 13,961 13,821 3 0.9831 13,669 13,438 4 0.9731 13,357 12,998 5 0.9593 13,028 12,497 6 0.9413 12,683 11,938 7 0.9188 12,325 11,324 8 0.8918 11,956 10,662 9 0.8604 11,578 9,961 10 0.8252 11,193 9,237 11 0.7866 10,804 8,499 12 0.7170 10,413 7,466 13 0.6125 10,022 6,138 14 0.5094 9,633 4,907 15 0.4142 9,249 3,831 16 0.3308 8,871 2,934 17 0.2604 8,502 2,214 18 0.2028 8,144 1,652 19 0.1565 7,799 1,220 20 0.1200 7,469 896 21 0.0916 7,157 656 22 0.0696 6,866 478 23 0.0527 6,596 348 24 0.0399 6,350 253 25 0.0301 6,131 185 26 0.0227 5,940 135

Estimated Passenger Car Lifetime VMT 161,847Economic Assumptions Used in the Agencies’ Analyses

17 Table 4-3 Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT) by Age for Light Trucks

VEHICLE AGE ESTIMATED SURVIVAL FRACTION ESTIMATED

ANNUAL VMT SURVIVALWEIGHTED ANNUAL VMT 1 0.9950 16,085 16,004 2 0.9741 15,782 15,374 3 0.9603 15,442 14,829 4 0.9420 15,069 14,195 5 0.9190 14,667 13,479 6 0.8913 14,239 12,691 7 0.8590 13,790 11,845 8 0.8226 13,323 10,960 9 0.7827 12,844 10,053 10 0.7401 12,356 9,145 11 0.6956 11,863 8,252 12 0.6501 11,369 7,391 13 0.6042 10,879 6,573 14 0.5517 10,396 5,735 15 0.5009 9,924 4,971 16 0.4522 9,468 4,281 17 0.4062 9,032 3,669 18 0.3633 8,619 3,131 19 0.3236 8,234 2,665 20 0.2873 7,881 2,264 21 0.2542 7,565 1,923 22 0.2244 7,288 1,635 23 0.1975 7,055 1,393 24 0.1735 6,871 1,192 25 0.1522 6,739 1,026 26 0.1332 6,663 887 27 0.1165 6,648 774 28 0.1017 6,648 676 29 0.0887 6,648 590 30 0.0773 6,648 514 31 0.0673 6,648 447 32 0.0586 6,648 390 33 0.0509 6,648 338 34 0.0443 6,648 294 35 0.0385 6,648 256 36 0.0334 6,648 222 Estimated Lifetime Light Truck VMT 190,066Draft Joint Technical Support Document

18

4.2.4.1 Adjusting Vehicle Use for Future Fuel Prices

The estimates of average annual miles driven by pas senger cars and light trucks reported in Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

19 Table 4-2Draft Joint Technical Support Document

20

Table 4

- 2 Table 4

- 2 and Table 4-3 reflect the historically low gasolin e prices that prevailed at the time the 2001 NHTS was conducted. To account for the ef fect on vehicle use of subsequent increases in fuel prices, the estimates of annual vehicle use de rived from the NHTS were adjusted to reflect the forecasts of future gasoline prices reported in the AEO 2009 Reference Case. This adjustment accounts for the difference between the average pri ce per gallon of fuel forecast for each year over t he expected lifetimes of model year 2012-2016 passenge r cars and light trucks, and the average price that prevailed when the NHTS was conducted in 2001. The elasticity of annual vehicle use with respect to fuel cost per mile corresponding to the 10 percent fuel economy rebound effect used in this analysis (i.e., an elasticity of -0.10) was applied to the p ercent difference between each future year’s fuel p rices and those prevailing in 2001 to adjust the estimate s of vehicle use derived from the NHTS to reflect the effect of higher future fuel prices.

4.2.4.2 Ensuring Consistency with Growth in Total Vehicle U se
The estimates of annual miles driven by passenger c ars and light trucks at each age were also adjusted to reflect projected future growth in average vehicle use. Increases in the average number of miles cars and trucks are driven each yea r have been an important source of historical growth in total car and light truck use, and are expected to repres ent an important source future growth in total light-duty vehicle travel as well. As an illustration of the importance of growth in average vehicle use, the total number of miles driv en by passenger cars increased 35 percent from 1985 through 2005, equivalent to a compound an nual growth rate of 1.5 percent. 6 During that time, however, the total number of passenger c ars registered for in the U.S. grew by only about 0.3 percent annually. h Thus growth in the average number of miles automo biles are driven each year accounted for the remaining 1.2 percent ( = 1.5 percent - 0.3 percent) annual growth in total automobile use. i Further, the AEO 2009 Reference Case forecasts of total car and light truck use and of the number of cars and light truck s in use suggest that their average annual use will continue to increase gradually from 2010 throu gh 2030.

In order to develop reasonable estimates of future growth in average car and light truck use, NHTSA calculated the rate of growth in the mil eage schedules shown in

h A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accoun ted for a small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in serv ice to various ages was computed from R.L. Polk vehicle registrati on data for 1977 through 2005 by the agency’s Cente r for Statistical Analysis.

i See supra note [2 above here] Field Code Changed

Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

21 Table 4-4Draft Joint Technical Support Document

22

Table 4

- 4 Table 4

- 4 and Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

23 Table 4-5Draft Joint Technical Support Document

24

Table 4

- 5 Table 4

- 5 that would be necessary for total car and light tr uck travel to increase at the rate forecast in the AEO 2009 Reference Case. This rate was calculated in a manner that is also consistent with future changes in the overall size and age distributions of the U.S. passenger car and light truck fleets that are implied by the agen cy’s adjusted forecasts of total car and light truck sales, together with the survival rates repor ted in Table 4-2Table 4

- 2 Table 4

- 2 and Table 4-3. The growth rate in average annual car and lig ht truck use produced by this calculation is approximately 1.1 percent per year. j This rate was applied to the mileage figures repo rted in Table 4-2Table 4

- 2 Table 4

- 2 and Table 4-3 to estimate annual mileage by age du ring each calendar year of the expected lifetimes of MY 2012- 2016 cars and light trucks.

j It was not possible to estimate separate growth ra tes in average annual use for cars and light trucks , because of the significant reclassification of light truck models as passenger cars discussed previously. Field Code Changed
Field Code Changed

Field Code Changed Field Code Changed

Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

25 Table 4-4Draft Joint Technical Support Document

26

Table 4

- 4 Table 4

- 4 and Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

27 Table 4-5Draft Joint Technical Support Document

28

Table 4

- 5 Table 4

- 5 report the results of applying the adjustments for both future fuel prices and annual growth in car and light truck use to the fig ures reported previously. While the adjustment for future fuel prices reduces average mileage at e ach age from the values shown previously, the adjustment for expected future growth in average ve hicle use increases it. As Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

29 Table 4-4Draft Joint Technical Support Document

30

Table 4

- 4 Table 4

- 4 and Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

31 Table 4-5Draft Joint Technical Support Document

32

Table 4

- 5 Table 4

- 5 show, the net effect of these two adjustments is t o increase expected lifetime mileage significantly; for passenger cars, this fig ure rises to 190,971 miles from the 161,847 miles reported previously in Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

33 Table 4-2Draft Joint Technical Support Document

34

Table 4

- 2 Table 4

- 2 (or by 18 percent), while expected lifetime mileag e for light trucks increases from the 190,066 miles reported previously in Tabl e 4-3 to 221,199 miles (16 percent).

Separate adjustments for projected fuel prices and growth in car and light truck use were made for each calendar year from 2012 through 2030. The values reported in the tables below are the averages of the adjusted values of annual c ar and light truck use by age for calendar years 2012-2030. These averages were used to analyze the impacts of improved fuel efficiency over the lifetimes of MY 2012-2016 cars and light trucks . In contrast, the estimated fuel savings and other impacts of improved fuel efficiency reported for individual calendar years used the adjusted values of car and light truck use by age d uring those specific calendar years.

As previously stated, the estimates of survival-wei ghted mileage decline to less than 1,000 miles per year after age 20 for cars and afte r age 27 for light trucks. Thus they have relatively little impact on lifetime fuel savings o r other benefits from higher fuel economy, particularly after discounting the benefits that oc cur in those distant future years to their present values. Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

35 Table 4-4 Survival Rates and Adjusted Annual Vehicl e-Miles Traveled (VMT) by Age for Passenger Cars

Vehicle Age Estimated Survival Fraction Estimated Annual

VMT Survival-Weighted

Annual VMT 1 0.9950 16,932 16,847 2 0.9900 16,603 16,437 3 0.9831 16,257 15,983 4 0.9731 15,814 15,389 5 0.9593 15,414 14,787 6 0.9413 14,993 14,113 7 0.9188 14,545 13,364 8 0.8918 14,105 12,578 9 0.8604 13,624 11,722 10 0.8252 13,192 10,886 11 0.7866 12,668 9,964 12 0.7170 12,222 8,763 13 0.6125 11,705 7,170 14 0.5094 11,191 5,700 15 0.4142 10,727 4,443 16 0.3308 10,283 3,402 17 0.2604 9,878 2,572 18 0.2028 9,482 1,923 19 0.1565 9,090 1,423 20 0.1200 8,691 1,043 21 0.0916 8,366 766 22 0.0696 8,126 566 23 0.0527 8,003 422 24 0.0399 7,774 310 25 0.0301 7,587 228 26 0.0227 7,424 169

Adjusted Lifetime Passenger Car VMT 190,971Draft Joint Technical Support Document

36

Table 4-5 Survival Rates and Adjusted Annual Vehicle-Miles Tr aveled (VMT) by Age for Light Trucks

Vehicle Age Estimated Survival

Fraction Estimated Annual

VMT Survival-Weighted

Annual VMT 1 0.9950 18,847 18,752 2 0.9741 18,408 17,931 3 0.9603 18,050 17,333 4 0.9420 17,575 16,556 5 0.9190 17,142 15,753 6 0.8913 16,593 14,790 7 0.8590 16,095 13,826 8 0.8226 15,493 12,745 9 0.7827 14,891 11,655 10 0.7401 14,336 10,610 11 0.6956 13,689 9,522 12 0.6501 13,160 8,555 13 0.6042 12,554 7,585 14 0.5517 11,945 6,590 15 0.5009 11,342 5,681 16 0.4522 10,822 4,894 17 0.4062 10,383 4,218 18 0.3633 9,900 3,597 19 0.3236 9,433 3,053 20 0.2873 9,033 2,595 21 0.2542 8,692 2,210 22 0.2244 8,499 1,907 23 0.1975 8,246 1,629 24 0.1735 8,261 1,433 25 0.1522 8,066 1,228 26 0.1332 8,066 1,074 27 0.1165 8,101 944 28 0.1017 8,098 824 29 0.0887 8,096 718 30 0.0773 8,095 626 31 0.0673 8,093 545 32 0.0586 8,092 474 33 0.0509 8,086 412 34 0.0443 8,080 358 35 0.0385 8,064 310 36 0.0334 8,050 269

Adjusted Lifetime Light Truck VMT 221,199Economic Assumptions Used in the Agencies’ Analyses

37 

### 4.2.5 Accounting for the Fuel Economy Rebound Effect

The rebound effect refers to the fraction of fuel savings expected to result from an increase in vehicle fuel economy that is offset by additional vehicle use. The increase in vehicle use that stems from improved fuel economy occurs be cause vehicle owners respond to the resulting reduction in vehicle fuel consumption and operating costs by driving slightly more.

The magnitude of the rebound effect is one of the d eterminants of the actual fuel savings that are likely to result from adopting stricter fu el economy or emissions standards, and thus an important parameter affecting EPA’s and NHTSA’s eva luation of alternative standards for future model years. It can be measured directly by estima ting the elasticity of vehicle use with respect to fuel economy itself, or indirectly by the elasti city of vehicle use with respect to fuel cost per mile driven. k

When expressed as a positive percentage, either o f these parameters gives the fraction of fuel savings that would otherwise resul t from adopting stricter standards, but is offset by the increase in fuel consumption that results wh en vehicles with increased fuel economy are driven more.

The fuel economy rebound effect for light-duty vehi cles has been the subject of a large number of studies since the early 1980s. Although they have reported a wide range of estimates of its exact magnitude, these studies generally con clude that a significant rebound effect occurs when vehicle fuel efficiency improves. l The most common approach to estimating its magnit ude has been to analyze household survey data on vehicl e use, fuel consumption, fuel prices (often obtained from external sources), and other determin ants of household travel demand to isolate the response of vehicle use to higher fuel economy. Other studies have relied on econometric analysis of annual U.S. data on vehicle use, fuel e conomy, fuel prices, and other variables to identify the response of total or average vehicle u se to changes in fleet-wide average fuel economy and its effect of fuel cost per mile driven . Two recent studies analyzed yearly variation in vehicle ownership and use, fuel prices, and fuel economy among individual states over an extended time period in order to measure the respon se of vehicle use to changing fuel economy. m

This section surveys these previous studies, summar izes recent work on the rebound effect, and explains the basis for the 10 percent r ebound effect EPA and NHTSA are using in this proposed rulemaking. 7

k Fuel cost per mile is equal to the price of fuel i n dollars per gallon divided by fuel economy in mil es per gallon, so this figure declines when a vehicle’s fuel economy increases.

l Some studies estimate that the long-run rebound ef fect is significantly larger than the immediate res ponse to increased fuel efficiency. Although their estimate s of the adjustment period required for the rebound effect to reach its long-run magnitude vary, this long-run effect i s most appropriate for evaluating the fuel savings and emissions reductions resulting from stricter standards that w ould apply to future model years.

m In effect, these studies treat U.S. states as a da ta “panel” by applying appropriate estimation proce dures to data consisting of each year’s average values of these v ariables for the separate states.Draft Joint Technical Support Document

38

4.2.5.1 Summary of Historical Literature on Rebound Effect

It is important to note that a majority of the stud ies previously conducted on the rebound effect rely on data from the 1950-1990s. While the se older studies provide valuable information on the potential magnitude of the rebound effect, s tudies that include more recent information (e.g., data within the last decade) may provide mor e reliable estimates of how this proposal will affect future driving behavior. Therefore, the more recent studies have been described in more detail below.

Estimates based on aggregate U.S. vehicle travel da ta published by the U.S. Department of Transportation, Federal Highway Administration, covering the period from roughly 1950 to 1990, have found long-run rebound effects on the or der of 10-30 percent. Some of these studies are summarized in the following table.

Table 4-6 Estimates of the Rebound Effect

Using U.S. Aggregate Time-Series Data on Vehicle Tr avel 1

AUTHOR (YEAR) SHORTRUN LONG-RUN TIME PERIOD

Mayo & Mathis (1988) 22% 26% 1958-84

Gately (1992) 9% 9% 1966-88 Greene (1992) Linear 5-

19% Log-linear 13% Linear 5-19%

Log-linear 13% 1957-89

Jones (1992) 13% 30% 1957-89 Schimek (1996) 5-7% 21-29% 1950-94

1 Source: Sorrell and Dimitropolous (2007) table 4.6.

Table 4-7 Estimates of the Rebound Effect Using U.S . State Level Data 1

AUTHOR (YEAR) SHORT-RUN LONG-RUN TIME PERIOD

Haughton & Sarkar (1996) 9-16% 22% 1973-1992

Small and Van Dender (2007) 4.5% 22% 1961-2001

1 Source: Sorrell and Dimitropolous (2007) table 4.7.

While national ( Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

39 Table 4-6

Table 4

- 6 Table 4

- 6 ) and state level ( Table 4-7Table 4

- 7 Table 4

- 7 ) data have found relatively consistent long-run estimates of the reb ound effect, household surveys display more variability ( Table 4-8Table 4

- 8 Field Code Changed

Field Code ChangedDraft Joint Technical Support Document

40

Table 4

- 8 ). There are several possible explanations for thi s larger variability. One explanation is that some of these studies do not include vehicle a ge as an explanatory variable, thus leading to omitted variable bias. 8 Another explanation is that it is difficult to di fferentiate between the impacts of residential density and fuel prices, sin ce households with higher fuel prices are more likely to be in urban areas. 9

Table 4-8 Estimates of the Rebound Effect Using U.S. Survey D ata

AUTHOR (YEAR) SHORTRUN LONG-RUN TIME PERIOD

Goldberg (1966) 0% CES 1984-90 Greene (1999) 23% EIA RTECS 1979-1994 Pickrell & Schimek (1999) 4-34% NPTS 1995 Single year

Puller & Greening (1999) 49% CES 1980-90 Single year, cross-sectional West (2004) 87% CES 1997 Single year

An important distinction among studies of the rebou nd effect is whether they assume that the effect is constant, or varies over time in resp onse to the absolute levels of fuel costs, personal income, or household vehicle ownership. Most studi es using aggregate annual data for the U.S. assume a constant rebound effect, although some of these studies test whether the effect can vary as changes in retail fuel prices or average fuel ec onomy alter fuel cost per mile driven. Many studies using household survey data estimate signif icantly different rebound effects for households owning varying numbers of vehicles, alth ough they arrive at differing conclusions about whether the rebound effect is larger among ho useholds that own more vehicles. Finally, one recent study using state-level data concludes t hat the rebound effect varies directly in response to changes in personal income and the degr ee of urbanization of U.S. cities, as well as fuel costs.

In order to provide a more comprehensive overview o f previous estimates of the rebound effect, EPA and NHTSA reviewed 22 studies of the re bound effect conducted from 1983 through 2005. The agencies then performed a detailed analy sis of the 66 separate estimates of the longrun rebound effect reported in these studies, which is summarized in Table 4-9Table 4

- 9 Table 4

- 9 below. n

As the table indicates, these 66 estimates of the long-run rebound effect range from as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. Limiting the sample to 50 estimates reported in the 17 published studies of the rebound effect yields the same range, but a slightly higher mean estimate (24 perc ent).

n In some cases, NHTSA derived estimates of the overa ll rebound effect from more detailed results report ed in the studies. For example, where studies estimated dif ferent rebound effects for households owning differ ent numbers of vehicles but did not report an overall value, we computed a weighted average of the reported values using the distribution of households among vehicle ownership categories. Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

41 The type of data used and authors’ assumption about whether the rebound effect varies over time have important effects on its estimated m agnitude. The 34 estimates derived from analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the long-run rebound effect, while the mean of 23 estimates base d on household survey data is considerably larger (31 percent), and the mean of 9 estimates ba sed on state data (25 percent) is close to that for the entire sample. The 37 estimates assuming a constant rebound effect produce a mean of 23 percent, identical to the mean of the 29 estimat es reported in studies that allowed the rebound effect to vary in response to fuel prices, vehicle ownership, or household income.

Table 4-9 Summary Statistics for Estimates of the R ebound Effect

Range Distribution Category of Estimates Number of Studies Number of

Estimates Low High Median Mean Std. Dev.
All Estimates 22 66 7% 75% 22% 23% 14%
Published Estimates 17 50 7% 75% 22% 24% 14%
U.S. Time-Series Data 7 34 7% 45% 14% 18% 9% Household Survey Data 13 23 9% 75% 31% 31% 16%
Pooled U.S. State Data 2 9 8% 58% 22% 25% 14% Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16 % Variable Rebound Effect: (1) 10 29 10% 45% 23% 23% 10%

4.2.5.2 Summary of Recent Studies and Analyses of the Rebou nd Effect

More recent studies since 2007 indicate that the im pacts of fuel prices and fuel economy have been decreasing over time as incomes increase and real gasoline prices fall. The theoretical argument for why the rebound effect should vary is that the responsiveness to the fuel cost of driving will be larger when it is a larger proporti on of the total cost of driving. As incomes rise (or fuel prices fall), the responsiveness to the fu el cost per mile of driving will decrease if people view the time cost of driving – which is likely to be related to their income levels – as a larger component of the total cost.

Small and Van Dender combined time series data for each of the 50 States and the District of Columbia to estimate the rebound effect , allowing the magnitude of the rebound to vary over time. 10 For the time period from 1966-2001, their study f ound a long-run rebound effect of about 22 percent, which is consistent wit h previously published studies. But for the most recent five year period (1997-2001), the long- run rebound effect decreased to 11 percent. Furthermore, when the authors updated their estimat es with data through 2004, the long-run rebound effect for the most recent five year period (2000-2004) dropped to 6 percent. 11

New research conducted by David Greene in 2008-2009 at EPA's request further supports the theory that the magnitude of the rebound effect is declining over time. 12 Over the entire time period analyzed (1966-2007), Greene found that fuel economy had no statistically significant impact on VMT. One potential explanation for Green e’s conclusion is that there is limited variation in fleet-wide fuel economy in the later t ime period (1982-2007), so that most of the variation in VMT over that period is attributed to factors other than fuel economy. While SmallDraft Joint Technical Support Document

42

and Van Dender did not find a statistically signifi cant coefficient for fuel efficiency, they did find a statistically significant coefficient for th e price of fuel. They then tested the hypothesis that the elasticity of vehicle travel with respect to the price of fuel was equal to its elasticity wi th respect to the rate of fuel consumption (gallons pe r mile), and found that the data could not reject that hypothesis. As a consequence, they estimated the rebound effect as the elasticity of vehicle travel with respect to fuel cost per mile, which co nstrains these elasticities to be equal in magnitude. Greene also attempted to re-estimate th e Small and Van Dender results using his national data set. When using Greene’s preferred f unctional form, the projected rebound effect is approximately 12 percent in 2007, and drops to 10 p ercent in 2010, 9 percent in 2016 and 8 percent in 2030.

4.2.5.3 NHTSA analysis of the rebound effect

To provide additional insight into the rebound effe ct for the purposes of this rulemaking, NHTSA developed several new estimates of its magnit ude. These estimates were developed by estimating and testing several econometric models o f the relationship between vehicle milestraveled and factors that influence it, including h ousehold income, fuel prices, vehicle fuel efficiency, road supply, the number of vehicles in use, vehicle prices, and other factors.

As the study by Small and Van Dender pointed out, i t is important to account for the effect of fuel prices when attempting to estimate t he rebound effect. Failing to control for changes in fuel prices is likely to bias estimates of the rebound effect. Therefore, changes in fuel prices are taken into account in Volpe’s analysis o f the rebound effect. Several different approaches were used to estimate the fuel economy r ebound effect for light duty vehicles, including single equation OLS regressions, multiple equation systems estimated using two- and three-stage least squares regression, vector autore gression, and vector error-correction models. With the except of single-equation regressions esti mated using OLS, all of these procedures attempt to account for the endogenous relationship of fuel efficiency to fuel prices.

The results from each of these approaches are prese nted in Table 4-10Table 4

- 10 Table 4

- 10 below. The table reports the value of the rebound effect calculated over the entire period from 1950 through 2006, as well as for the final ye ar of that period. In addition, the table presents forecasts of the average rebound effect be tween 2010 and 2030, which utilize forecasts of personal income, fuel prices, and fuel efficienc y from EIA’s AEO 2009 Reference Case.

The results of NHTSA’s analysis are broadly consist ent with the findings from previous research summarized above. The historical average long-run rebound effect is estimated to range from 16-30 percent, and comparing these estimates t o its calculated values for 2006 (which range from 8-14 percent) gives some an indication that it is declining in magnitude. The forecast values of the rebound effect shown in the table als o suggest that this decline is likely to continue through 2030, as they range from 4-16 percent. Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

43 Table 4-10 Summary of NHTSA Estimates of the Rebound Effect

Rebound Effects: Model VMT

Measure Variables Included in VMT Equation Estimation Technique 1950-

2006 2006 2010-

2030*

Small-Van Dender single VMT equation annual VMT

per adult fuel cost per mile, per Capita income, vehicle stoc k, road

miles per adult, fraction of population that is adu lt, fraction of population living in urban areas, fract ion of population living in urban areas with heavy rail, d ummy variables for fuel rationing, time trend OLS 33.0% 15.8% 8.0%

Small-Van Dender three-equation system annual VMT

per adult fuel cost per mile, per Capita income, vehicle stoc k, road

miles per adult, fraction of population that is adu lt, fraction of population living in urban areas, fract ion of population living in urban areas with heavy rail, d ummy variables for fuel rationing, time trend 3SLS 21.6% 5.8% 3.4%

Single-equation VMT model annual VMT

per adult personal income, road miles per Capita, time trend OLS 18.4% 11.7% 9.2% Single-equation VMT model annual VMT

per vehicle fuel cost per mile, personal income, road miles per

Capita, time trend OLS 17.6% 15.2% 15.7%
Single-equation VMT model annual VMT

per adult fuel cost per mile, personal income, road miles per

Capita, dummy variables for fuel rationing, time tr end OLS 34.0% 20.8% 13.6%

Single-equation VMT model annual VMT

per vehicle fuel cost per mile, personal income, vehicles per r oad

mile, % of fleet manufactured under CAFE standards, new vehicle prices IV (for fuel cost per mile) 16.3% 9.2% 7.0%

Three-equation system for VMT, fuel efficiency, and vehicle stock annual VMT

per vehicle fuel cost per mile, personal income, vehicles per c apita,

vehicles per road mile, fraction of adult populatio n licensed to drive, new vehicle prices, % of fleet manufactured under CAFE standards 2SLS 29.5% 13.4% 15.9%

Three-equation system for VMT, fuel efficiency, and vehicle stock annual VMT

per vehicle fuel cost per mile, personal income, vehicles per c apita,

vehicles per road mile, fraction of adult populatio n licensed to drive, new vehicle prices, % of fleet manufactured under CAFE standards 3SLS 29.8% 13.7% 16.2%

Three-equation system for VMT, annual VMT

per vehicle fuel cost per mile, personal income, vehicles per c apita,

vehicles per road mile, fraction of adult populatio n Vector autoregression 19.9% 10.8%

4.2.5.4 Basis for Rebound Effect Used by EPA and NHTSA in T his Proposed Rule

Based on a combination of historical estimates of t he rebound effect and more recent analyses conducted by EPA and NHTSA, an estimate of 10 percent for the rebound effect was used for this rulemaking. Although this value repr esents the lower bound of the historical range of values for the rebound effect, more recent analy sis strongly suggests that the rebound effect is declining over time. As part of this proposal, we are also providing information on options for projecting the rebound effect in the future. This projection approach, as described in the Small Report, 13 takes into account changes in income and gasoline prices when estimating the magnitude of the rebound effect in the future. We will continue to evaluate this methodology between the proposed and final rules, and we will t ake into account public comments when determining whether it is more appropriate to use t his projection approach for predicting the rebound effect.

EPA and NHTSA also seek s comment on other alternatives for estimating the r ebound effect. As one illustration, variation in the pric e per gallon of gasoline directly affects the permile cost of driving, and drivers may respond just as they would to a change in the cost of driving resulting from a change in fuel economy, by varying the number of miles they drive. Because vehicles’ fuel economy is fixed in the shor t run, variation in the number of miles driven in response to changes in fuel prices will be refle cted in changes in gasoline consumption. Under the assumption that drivers respond similarly to changes in the cost of driving whether they are caused by variation in fuel prices or fuel economy, the short-run price elasticity of demand for gasoline – which measures the sensitivit y of gasoline consumption to changes in its Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 ptDraft Joint Technical Support Document

44

price per gallon – may provide some indication abou t the magnitude of the rebound effect itself. NHTSA The agencies also invite s comment on the extent to which the short run elast icity of demand for gasoline with respect to its price can p rovide useful information about the size of the rebound effect. Specifically, the agenc iesy seek s comment on whether it would be appropriate to use the price elasticity of demand for gasoline, or other a lternative approaches, to guide its their choice of a value for the rebound effect.



### 4.2.6 Benefits from Increased Vehicle Use

The increase in vehicle use from the rebound effect provides additional benefits to their owners, who may make more frequent trips or travel farther to reach more desirable destinations. This additional travel provides benefits to drivers and their passengers by improving their access to social and economic opportunities away from home . As evidenced by their decisions to make more frequent or longer trips when improved fuel ec onomy reduces their costs for driving, the benefits from this additional travel exceed the cos ts drivers and passengers incur in making more frequent or longer trips.

The analysis estimates the economic benefits from i ncreased rebound-effect driving as the sum of fuel costs drivers incur plus the consum er surplus they receive from the additional accessibility it provides. o Because the increased in travel depend on the ext ent of improvement in fuel economy, the value of benefits it provides differs among model years and alternative CAFE standards. Under even those alternatives that would impose the highest standards, however, the magnitude of these benefits represents a small fraction of total benefits.



### 4.2.7 Added Costs from Increased Vehicle Use

Although it provides some benefits to drivers, incr eased vehicle use associated with the rebound effect also contributes to increased traffi c congestion, motor vehicle accidents, and highway noise. Depending on how the additional tra vel is distributed over the day and on where it takes place, additional vehicle use can contribu te to traffic congestion and delays by increasing traffic volumes on facilities that are already heav ily traveled during peak periods. These added delays impose higher costs on drivers and other veh icle occupants in the form of increased travel time and operating expenses. Because drivers do no t take these added costs into account in deciding when and where to travel, they must be acc ounted for separately as a cost of the added driving associated with the rebound effect.

Increased vehicle use due to the rebound effect may also increase the costs associated with traffic accidents. Drivers may take account o f the potential costs they (and their passengers) face from the possibility of being involved in an a ccident when they decide to make additional trips. However, they probably do not consider all of the potential costs they impose on occupants of other vehicles and on pedestrians when accidents occur, so any increase in these

o The consumer surplus provided by added travel is e stimated as one-half of the product of the decline in fuel cost per mile and the resulting increase in the annual n umber of miles driven. Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 pt

Formatted: Space Before: 0 pt, After: 0 pt, Hyphenate, Don't adjust space between Latin and Asian text, Don't adjust space between Asian text and numbers

Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 pt

Formatted: Font: Times New Roman, 12 ptEconomic Assumptions Used in the Agencies’ Analyses

45 “external” accident costs must be considered as ano ther cost of additional rebound-effect driving. Like increased delay costs, any increase in these e xternal accident costs caused by added driving is likely to depend on the traffic conditions under which it takes place, since accidents are more frequent in heavier traffic (although their severit y may be reduced by the slower speeds at which heavier traffic typically moves).

Finally, added vehicle use from the rebound effect may also increase traffic noise. Noise generated by vehicles causes inconvenience, irritat ion, and potentially even discomfort to occupants of other vehicles, to pedestrians and oth er bystanders, and to residents or occupants of surrounding property. Because these effects are un likely to be taken into account by the drivers whose vehicles contribute to traffic noise, they re present additional externalities associated with motor vehicle use. Although there is considerable uncertainty in measuring their value, any increase in the economic costs of traffic noise res ulting from added vehicle use must be included together with other increased external costs from t he rebound effect.

EPA and NHTSA rely on estimates of congestion, acci dent, and noise costs caused by automobiles and light trucks developed by the Feder al Highway Administration to estimate the increased external costs caused by added driving du e to the rebound effect. 14 NHTSA employed these estimates previously in its analysis accompan ying the MY 2011 final rule, and continues to find them appropriate for this NPRM after reviewing the procedures used by FHWA to develop them and considering other available estimates of t hese values. They are intended to measure the increases in costs from added congestion, property damages and injuries in traffic accidents, and noise levels caused by automobiles and light trucks that are borne by persons other than their drivers (or “marginal” external costs).

Updated to 2007 dollars, FHWA’s “Middle” estimates for marginal congestion, accident, and noise costs caused by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 cents per vehicle-mile (for a total of 7.6 cents per mile), w hile those for pickup trucks and vans are 4.7 cents, 2.5 cents, and 0.1 cents per vehicle-mile (f or a total of 7.3 cents per mile). 15, p These costs are multiplied by the annual increases in automobil e and light truck use from the rebound effect to yield the estimated increases in congestion, acc ident, and noise externality costs during each future year.



### 4.2.8 Petroleum and Energy Security Impacts

4.2.8.1 Impact on U.S. Petroleum Imports

In 2008, U.S. petroleum import expenditures repres ented 21 percent of total U.S. imports of all goods and services. 16 In 2008, the United States imported 66 percent of the petroleum it

p The Federal Highway Administration’s estimates of these costs agree closely with some other recent es timates. For example, recent published research conducted by Resources for the Future (RFF) estimates marginal congestion and external accident costs for increased light-dut y vehicle use in the U.S. to be 3.5 and 3.0 cents p er vehicle-mile in year-2002 dollars. See Ian W.H. Parry and Kenneth A. Small, “Does Britain or the U.S. Have the Right Gasoline Tax?” Discussion Paper 02-12, Resources for the Fut ure, 19 and Table 1 (March 2002). Available at http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed July 27, 2009).Draft Joint Technical Support Document

46

consumed, and the transportation sector accounts fo r 70 percent of total U.S. petroleum consumption. This compares roughly to 37 percent o f petroleum from imports and 55 percent consumption of petroleum in the transportation sect or in 1975. 17 It is clear that petroleum imports have a significant impact on the U.S. econo my. Requiring lower-GHG vehicle technology and improved fuel economy in the U.S. is expected to lower U.S. petroleum imports.

4.2.8.2 Background on U.S. Energy Security

U.S. energy security is broadly defined as protect ing the U.S. economy against circumstances that threaten significant short- and long-term increases in energy costs. Most discussion of U.S. energy security revolves around the topic of the economic costs of U.S. dependence on oil imports. The problem is that the U.S. relies on sources of imported oil from potentially unstable sources. In addition, oil exp orters have the ability to raise the price of oil b y exerting monopoly power through the formation of a cartel, the Organization of Petroleum Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks and price spi kes. In 2008, U.S. imports of crude oil were $326 billion (in 2007$, see Figure 4-1Figure 4

- 1 Figure 4

- 1 ). Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

47 

### U.S. Expenditures on Crude Oil

0 100 200 300 400 500 600 700

1970 1975 1980 1985 1990 1995 2000 2005

Year(2007$ Billions) Domestic

Imported

Figure 4-1 U.S. Expenditures on Crude Oil from 1970 through 2008 q

One effect of the EPA/NHTSA joint proposal is that it promotes more efficient use of transportation fuels in the U.S. The result is tha t it reduces U.S. oil imports, which reduces both financial and strategic risks associated with a pot ential disruption in supply or a spike in the cost of a particular energy source. This reduction in r isks is a measure of improved U.S. energy security. For this proposal, an “oil premium” appr oach is utilized to identify those energysecurity related impacts which are not reflected in the market price of oil, and which are expected to change in response to an incremental ch ange in the level of U.S. oil imports.

4.2.8.2.1 Methodology Used to Estimate U.S. Energy Security B enefits

In order to understand the energy security implicat ions of reducing U.S. oil imports, EPA has worked with Oak Ridge National Laboratory (ORNL ), which has developed approaches for

q For historical data through 2006: EIA Annual Ener gy Review, various editions. For data 2006-2008: EIA Annual Energy Outlook (AEO ) 2009 (Update Reference (Stimulus) Base Case). See file "aeostimtab_11.xls" available at http://ww w.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.htmlDraft Joint Technical Support Document

48

evaluating the social costs and energy security imp lications of oil use. The energy security estimates provide below are based upon a methodolog y developed in a peer-reviewed study entitled, “ The Energy Security Benefits of Reduced Oil Use, 20 06-2015 ,” completed in March 2008. This recent study is included as part of the docket for this rulemaking. 18 This ORNL study is an update version of the approach to estim ating the energy security benefits of U.S. oil import reductions developed in an ORNL 1997 Report by Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, entitled “ Oil Imports: An Assessment of Benefits and Costs. ”

When conducting this recent analysis, ORNL consider ed the full cost of importing petroleum into the U.S. The full economic cost is defined to include two components in addition to the purchase price of petroleum itself. These a re: (1) the higher costs for oil imports resulting from the effect of U.S. import demand on the world oil price and on OPEC market power ( i.e.
, the “demand” or “monopsony” costs); and (2) the ris k of reductions in U.S. economic output and disruption of the U.S. economy caused by sudden dis ruptions in the supply of imported oil to the U.S. ( i.e. , macroeconomic disruption/adjustment costs). Main taining a U.S. military presence to help secure stable oil supply from potentially vuln erable regions of the world was not included in this analysis because its attribution to particular missions or activities is difficult (as discussed further below). Section III.H.8.b of the preamble contains a detailed discussion of how the monopsony and macroeconomic disruption/adjustment c omponents were treated for this analysis.

As part of the process for using the ORNL energy se curity estimates, EPA sponsored an independent-expert peer review of this ORNL study. A report compiling the peer reviewers’ comments is provided in the docket. 19 In addition, EPA has worked with ORNL to address comments raised in the peer review and to develop e stimates of the energy security benefits associated with a reduction in U.S. oil imports for this proposal. In response to peer reviewer comments, ORNL modified its model by changing sever al key parameters involving OPEC supply behavior, the responsiveness of oil demand a nd supply to a change in the world oil price, and the responsiveness of U.S. economic output to a change in the world oil price.

For this proposal, ORNL further updated the energy security premium by incorporating the most recent oil price forecast and energy marke t trends in AEO 2009 into its model. In order for the energy security premium estimated to be use d in EPA’s VECTOR model, ORNL developed energy security estimates for a number of different years; e.g. , 2015, 2020, 2030, and 2040. NHTSA used just the 2015 estimates to estima te the energy security benefits associated with this rule.

For 2015, ORNL has estimated that the total energy security premium associated with a reduction of imported oil is $18.49/barrel. On a d ollar per gallon basis, energy security benefits for 2015 are $0.44/gallon. Based upon alternative sensitivities regarding OPEC supply behavior, and the responsiveness of oil demand and supply to a change in the world oil price, the energy security premium ranges from $9.80 to $28.08/barrel . In terms of dollars per gallon, the energy security premium ranges from $0.23/gallon to $0.67/ gallon. Please refer to Table 4-11Table Field Code Changed

Field Code Changed

Formatted: Font: Not Bold

Formatted: Font: Not BoldEconomic Assumptions Used in the Agencies’ Analyses

49 4

- 11 Table 4

- 11 for similar information for years 2015, 2020, 2030 and 2040, r as well as a breakdown of the components of the energy security premium for each year. The components of the energy security premium and their values are di scussed below.

Table 4-11 Energy Security Premium in 2015, 2020, 2030 and 204 0
(2007$/Barrel)

Year Monopsony

(Range) Macroeconomic

Disruption/Adjustment Costs

(Range) Total Mid-Point (Range)

2015 $11.79

($4.26 - $21.37) $6.70 $3.11 – $10.67 $18.49 $9.80 - $28.08 2020 $12.31

($4.46 - $22.53) $7.62 ($3.77 – $12.46) $19.94 ($10.58 - $30.47) 2030 $10.57

($3.84 – 18.94) $8.12 ($3.90 – $13.04) $18.69 ($10.52 - $27.89) 2040 $10.57

($3.84 – $18.94) $8.12 ($3.90 – $13.04) $18.69 ($10.52 - $27.89)

4.2.8.2.1.1 Effect of Oil Use on Long-Run Oil Price , U.S. Import Costs, and

Economic Output

The first component of the full economic costs of i mporting petroleum into the U.S. follows from the effect of U.S. import demand on th e world oil price over the long-run. Because the U.S. is a sufficiently large purchaser of forei gn oil supplies, its purchases can affect the world oil price. This monopsony power means that increas es in U.S. petroleum demand can cause the world price of crude oil to rise, and conversely, t hat reduced U.S. petroleum demand can reduce the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the increased availability and use of other transportat ion fuels, is the potential decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illus trated with an example. If the U.S. imports 10 million barrels per day at a world oil p rice of $50 per barrel, its total daily bill for oi l imports is $500 million. If a decrease in U.S. imp orts to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the dail y U.S. oil import bill drops to $441 million (9 million barrels times $49 per barrel). While the w orld oil price only declines $1, the resulting decrease in oil purchase payments of $59 million pe r day ($500 million minus $441 million) is equivalent to an incremental benefit of $59 per bar rel of oil imports reduced, or $10 more than the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost premium” represents the incremental external benefi ts to the U.S. for avoided import costs beyond the price paid oil purchases. This addition al benefit arises only to the extent that reduction in U.S. oil imports affects the world oil price. ORNL estimates this component of the

r AEO 2009 forecasts energy market trends and values only to 2030. The energy security premia post-203 0 were assumed to be the 2030 estimate. Formatted: Font: Not BoldDraft Joint Technical Support Document

50

energy security benefit in 2015 to be $11.79/barrel , with a range of $4.26 – $21.37/barrel of imported oil reduced.

4.2.8.2.1.2 Short-Run Disruption Premium from Expec ted Costs of Sudden Supply

Disruptions

The second component of the oil import premium, the “macroeconomic disruption/adjustment costs premium,” arises from t he effect of oil imports on the expected cost of disruptions. A sudden increase in oil prices tr iggered by a disruption in world oil supplies has two main effects: (1) it increases the costs of imp orts in the short run, further expanding the transfer of U.S. wealth to foreign producers, and ( 2) it can lead to macroeconomic contraction, dislocation and Gross Domestic Product (GDP) losses . ORNL estimates the composite estimate of these two factors that comprise the macroeconomi c disruption/adjustment costs premium to be $6.70/barrel in 2015, with a range of $3.11 – $10.6 7/barrel of imported oil reduced.

4.2.8.2.1.2.1 Higher Costs of Oil Imports and Wealt h Transfer during Shocks

During oil price shocks, the higher price of import ed oil causes increased payments for imports and a transfer of wealth from the U.S to oi l exporters. This increased claim on U.S. economic output is a loss to the U.S. that is separ ate from and additional to any reduction in economic output due to the shock. The increased we alth transfer during shocks is counted as a loss to the degree that the expected price increase is not anticipated and internalized by oil

consumers.
4.2.8.2.1.2.2 Macroeconomic Costs: Potential Output Loss and Dislocation/Adjustment Costs

Macroeconomic losses during price shocks reflect bo th aggregate output losses and “allocative” losses. The former are a reduction in the level of output that the U.S. economy can produce fully using its available resources; and th e latter stem from temporary dislocation and underutilization of available resources due to the shock, such as labor unemployment and idle plant capacity. The aggregate output effect, a red uction in “potential” economic output, will last so long as the price is elevated. It depends on th e extent and duration of any disruption in the world supply of oil, since these factors determine the magnitude of the resulting increase in prices for petroleum products, as well as whether a nd how rapidly these prices return to their predisruption levels.

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs associated with dislocated energy markets. Because supply disruptions and resulting price increases occur suddenly, empirical evidence shows they also impose additional costs on businesses and households which must adjust their u se of petroleum and other productive factors more rapidly than if the same price increase had oc curred gradually. Dislocational effects include the unemployment of workers and other resou rces during the time needed for their intersectoral or interregional reallocation, and pa uses in capital investment due to uncertainty. These adjustments temporarily reduce the level of e conomic output that can be achieved even below the “potential” output level that would ultim ately be reached once the economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on businessesEconomic Assumptions Used in the Agencies’ Analyses

51 and households for making these adjustments reflect their limited ability to adjust prices, output levels, and their use of energy, labor and other in puts quickly and smoothly in response to rapid changes in prices for petroleum products.

Since future disruptions in foreign oil supplies ar e an uncertain prospect, each of the disruption cost components must be weighted by the probability that the supply of petroleum to the U.S. will actually be disrupted. Thus, the “ex pected value” of these costs – the product of the probability that a supply disruption will occur and the sum of costs from reduced economic output and the economy’s abrupt adjustment to sharp ly higher petroleum prices – is the relevant measure of their magnitude. Further, when assessin g the energy security value of a policy to reduce oil use, it is only the change in the expect ed costs of disruption that results from the policy that is relevant. The expected costs of dis ruption may change from lowering the normal ( i.e. , pre-disruption) level of domestic petroleum use a nd imports, from any induced alteration in the likelihood or size of disruption, or from alter ing the short-run flexibility (e.g., elasticity) of petroleum use.

In summary, the steps needed to calculate the disru ption or security premium are: 1) determine the likelihood of an oil supply disruptio n in the future; 2) assess the likely impacts of a potential oil supply disruption on the world oil pr ice; 3) assess the impact of the oil price shock on the U.S. economy (in terms of import costs and m acroeconomic losses); and 4) determine how these costs change with oil imports. The value of price spike costs avoided by reducing oil imports becomes the oil security portion of the pre mium.

4.2.8.2.2 Costs of Existing U.S. Energy Security Po licies

The last often-identified component of the full eco nomic costs of U.S. oil imports is the costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples are maintaining a military presence to help secure a stable oil supply from potentially vulnerable regions of the world and maintaining the Strategic Petroleum Reserve (SPR). The SPR is the largest stockpile of government-owned emergency cru de oil in the world. Established in the aftermath of the 1973-74 oil embargo, the SPR provi des the U.S. a response option should a disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet part of its International Energy Agency obligation to maintain emergency oil stocks, and it provides a national defense fuel reserve.

U.S. military costs are excluded from the analysis performed by ORNL because their attribution to particular missions or activities is difficult. Most military forces serve a broad range of security and foreign policy objectives. A ttempts to attribute some share of U.S. military costs to oil imports are further challenged by the need to estimate how those costs might vary with incremental variations in U.S. oil imports. S imilarly, while the costs for building and maintaining the SPR are more clearly related to U.S . oil use and imports, historically these costs have not varied in response to changes in U.S. oil import levels. Thus, while SPR is factored into the ORNL analysis, the cost of maintaining the SPR is excluded.Draft Joint Technical Support Document

52

4.2.8.2.3 Modifications to Analysis Based Upon Peer Reviewer Comments

The EPA commissioned ORNL to conduct a number of se nsitivity analyses to address the comments of the peer reviewers. Based upon the pee r reviewer comments, key parameters that influence the “oil import” premium were assessed. Since not all the comments were in agreement with each other, several ranges of differ ent parameters were developed for the analyses. These sensitivities used the most recent price forecasts and energy market trends available at the time the peer review was being con ducted and completed, the AEO 2007 Reference Case. Thus, the results presented below are suggestive of how the energy security premium is influenced by alternative assumptions of key parameters that influence world oil markets. A summary of the results of the analyses are shown in Table 4-12Table 4

- 12 Table 4

- 12

. Three key parameters were varied in order to assess their impacts on the oil import premium: (1) the response of OPEC supply, (2) the c ombined response of non-U.S., non-OPEC demand and supply and (3) the GDP response to a cha nge in the world oil as a result of reduced U.S. oil imports. The cases used updated supply/de mand elasticities for non-U.S./non-OPEC region after considering more recent estimates than those used in 1997 study. As a result, the total market responsiveness is greater than previou s ORNL estimates. Only small changes to the world oil price are anticipated from a substantial reduction in U.S. demand, on average, about $0.70/barrel for every million-barrels-per-day redu ction in demand.

In the ORNL framework, OPEC-behavior is treated par ametrically, with a wide range of possible responses represented by a range of supply elasticities. Case One in Table 4-12Table 4

- 12 Table 4

- 12 below refers to the AEO 2007 estimates of energy m arket trends and uses the elasticity parameters from the original 1997 ORNL s tudy. In Case Two, the OPEC supply elasticities range for 0.25 to 6 with a mean elasti city of 1.7. Case Three alters the distribution of the OPEC supply elasticities so that the mean elast icity is 2.2 instead of 1.7. With the more elastic OPEC oil supply in Case Three, the oil prem ium is lower. Alternatively, a candidate rule for OPEC strategic response behavior, adapted from a lead article on what behavior maximizes OPEC’s long run net revenue in a robust way, 20 would have OPEC responding to preserve its worldwide oil market share. This is presented as C ase Seven. Application of this rule instead of the range of OPEC supply responses used leads to an estimate of the oil import premium that is between Case Two and Case Three.

The second key parameter that was varied based upon peer reviewer comments was nonOPEC, non-U.S. demand and supply responsiveness to a change in the U.S. oil import demand and, hence, the world oil price. In Case Four, the mean non-U.S./non-OPEC demand and supply elasticities are taken to each be 0.3 in absolute v alue terms. When combined together, the net elasticity of import demand from this region — the non-U.S./non-OPEC region is — roughly 1.6. Case Five takes the Case Four assumptions of a more elastic OPEC supply behavior and combines those assumptions with the 1.6 net elastic ity of import demand for the non-U.S./nonOPEC region. Case Six looks at the consequences of a yet higher net elasticity of import demand — 2.28 — for the non-U.S./non-OPEC region. The impact on the oil import premium is relatively modest. Field Code Changed

Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

53 Cases Eight and Nine consider a reduced GDP elastic ity, the parameter which summarizes the sensitivity of GDP to oil price shoc ks. Several reviewers suggested a lower estimate for this parameter. In response to their comments, a couple of cases were examined where the GDP elasticity was lowered to 0.032 in co mparison to the original ORNL estimate of 0.0495. As anticipated, this change lowered the oi l import premium modestly. For example, compared with Case Four where OPEC supply is more e lastic, lowering the GDP elasticity with respect to the world oil price reduced the oil impo rt premium by roughly $0.40/barrel. This is because the GDP-dislocation component is only about one-quarter of the total premium, and there are offsetting changes in other components. The last case examined, Case Nine, looks at the consequences for the oil import premium with a reduced elasticity of GDP if OPEC attempts to maintain its share of the world oil market.

Clearly there is an unavoidable degree of uncertain ty about the magnitude of marginal economic costs from the U.S. importation of petrole um, and the size of the oil import premium. ORNL sought to reflect this with probabilistic risk analysis over key input factors, guided by the available literature and the best judgment of oil m arket experts. Cases shown in Table 4-12Table 4

- 12 Table 4

- 12 explore some reasonable variations in the ranges of input assumptions and the mean oil premium estimates vary in a fairly moderat e range between roughly $11 and $15/barrel of imported oil. On balance, Case Eight suggested a reasonable and cautious assessment of the premium value to ORNL, and is ORNL’s recommended ca se. This is based on a review of important driving factors, the numerical evaluation s and simulations over major uncertainties, and taking into consideration the many comments and suggestion from the reviewers, the EPA and other Agencies. This recommended case, and the premium range resulting from 90% of the simulated outcomes, encompasses a wide array of per spectives and potential market outcomes in response to a reduction of U.S. imports.

As mentioned previously, this recommended case reli ed on the most recent available projections of the U.S. and world oil market for th e next ten years based upon the AEO 2007 Reference Case. OPEC-behavior was treated parametr ically, with a wide range of possible responses represented by a wide range of supply ela sticities, from small to quite large. This recommended case recognized that the OPEC response is the most uncertain single element of this analysis. It could vary between inelastic def ense of output levels, or market share, or could be highly elastic in defense of price, probably at the expense of longer run cartel power and discounted net profits. The balance between possib le elastic and inelastic OPEC response was essentially even over a fairly wide range of elasti cities. ORNL concluded that this is the best way to estimate OPEC behavior until greater progres s can be made in synthesizing what insights are available from the evolving strategic game-theo retic and empirical research on OPEC behavior, and advancing that research. An alternat ive would have been to use OPEC strategic response behavior to maximize long-run net revenue, which may well correspond to marketshare preservation behavior ( e.g. , Case Seven), and a somewhat higher premium value.

Finally, ORNL’s recommended case used a GDP elastic ity range, the parameter which summarizes the sensitivity of GDP to oil price shoc ks, which is reduced compared to earlier estimates, and compared to the full range of histor ically-based estimates. This helped address the concerns of those who either question the concl usions of past empirical estimates or expect that the impacts of oil shocks may well be declinin g. Field Code ChangedDraft Joint Technical Support Document54Table 4-12 Summary Results – Oil Import Premium Under Various Cases ($2007/BBL)Component Statistic1) AEO2007Base Outlook,1997 StudyElasticities2) AEO2007Base Outlook,Wider Range ofOPEC SupplyElasticities3) Case 2 withRevised WiderRange of OPECSupplyElasticities4) Case 2 withUpdated NonOPECSupply/DemandElasticities5) Case 4 plusRevised WiderRange of OPECSupplyElasticities6) Case 4Variant withWider, HigherRange of NonU.S./Non-OPECSupply/DemandElasticities7) Case 6 withApplied StrategicOPEC BehavioralRule: MaintainMarket Share8) Case 4 withReduced GDPElasticity9) Case 7 withReduced GDPElasticityMean $5.57 $10.26 $8.16 $7.77 $6.52 $6.44 $9.36 $7.86 $9.42Range ($3.60 - $8.19) ($3.10 - $21.22) ($3.12 - $19.30) ($2.94 - $13.75) ($2.90 - $13.06) ($2.69 - $11.33) ($6.69 - $12.42) ($2.94 - $13.89) ($6.72 - $12.54)Mean $2.36 $2.33 $2.34 $1.92 $1.94 $1.93 $1.89 $2.20 $2.16Range ($0.51 - $4.66) ($0.58 - $4.57) ($0.66 - $4.61) ($0.39 - $3.75) ($0.38 - $3.84) ($0.42 - $3.70) ($0.44- $3.66) ($0.63 - $4.07) ($0.62 - $3.96)Mean $3.83 $3.70 $3.76 $3.42 $3.45 $3.47 $3.41 $2.67 $2.64Range ($1.06 - $6.69) ($1.04 - $6.57) ($1.06 - $6.74) ($0.92 - $6.23) ($0.94 - $6.10) ($0.92 - $6.13) ($0.87- $6.26) ($0.90 - $4.84) ($0.87 - $4.87)Mean $6.19 $6.03 $6.10 $5.34 $5.39 $5.40 $5.30 $4.87 $4.81Range ($2.94 - $10.01) ($2.94 - $9.75) ($2.91 - $9.85)($2.53 - $8.69) ($2.60 - $8.62) ($2.58 - $8.92) ($2.54- $8.74) ($2.23 - $7.85) ($2.25 - $7.77)Mean $11.75 $16.29 $14.27 $13.11 $11.90 $11.86 $14.65 $12.71 $14.23Range ($8.04 - $15.96) ($8.10 - $27.42) ($7.90 - $25.49) ($7.26 - $19.59) ($6.96 - $18.62) ($6.79 - $17.23) ($11.03 - $18.61) ($7.07 - $19.02) ($10.76 - $18.25)Total Premium, in $/Gallon Mean $0.28 $0.39 $0.34 $0.31 $0.28 $0.28 $0.35 $0.30 $0.34Price Reduction ($/MMBD) Mean $0.52 $1.04 $0.81 $0.75 $0.61 $0.60 $0.90 $0.75 $0.90Cases7) Applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy). This rule implies that OPEC Supply elasticity matches that of all non-OPEC supply. As aresult non-U.S. elasticity of import demand rangesfrom8) Variant on version Case 4, considered reduced GDP elasticity for future disruptions (range -0.01 to-0.054; midcase value -0.032; mean value is -0.032, reduced from mean value of -0.0495). OPECbehavior treated parametrically.9) Revise Case 7 (which applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy)) with reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032,reduc3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response isinelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76)4) Updated Case 2 supply/demand elasticities for non-OPEC region with more recent estimates. Elasticity of non-U.S. demand -0.2 to -0.4, with mean and mode -0.3, non-U.S. Supply = 0.2 to 0.4, withmean and mode 0.3, implying (mode) net elasticity of impo5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response isinelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76). Net elasticity of importdemand is -1.6 for the non-U.S./non-OPEC r6) Alternative to Case 4 with expanded (and higher)range of non-U.S. supply/demand elasticities. Elasticity of non-U.S. demand = -0.3 to -0.7, triangular distribution with mode -0.4 mean -0.467, elasticity ofnon-U.S. supply = 0.2 to 0.6, mode 0.3 and mEconomic Disruption/AdjustmentCostsTotal Mid-Point1) Based on AEO2007. Updated oil market outlook from AEO1994 Base Case to AEO2007 Base Case. Among other things, this means average crude price rises from $20.33 to $48.34. All elasticitiesmatch 1997 values. Non-U.S. elasticity of import demand = -0.2) AEO2007 Base Outlook, with wider range of OPEC supply elasticities, 0.25 to 6.0 and a mean elasticity of 1.76.Monopsony ComponentDisruption Import CostsDisruption Dislocation CostsEconomic Assumptions Used in the Agencies’ Analyses

55 4.2.8.2.4 Energy Security Premium Values for This P roposal

After the peer review process was completed, EPA wo rked with ORNL to further update the analysis for this proposed vehicle progr am. Using the same methodology as the peer reviewed model, but updating the analysis usin g AEO 2009 world oil price values, ORNL calculated energy security premium estimates f or the years 2015, 2020, and 2030. Since the AEO 2009 ends its forecast in the year 20 30, EPA assumed that the post-2030 energy security premium estimates were the same as the 2030 estimate. The results of this analysis are shown above in Table 4-11Table 4

- 11 Table 4

- 11 in Chapter 4.2.8.

4.2.8.3 The Impact of Fuel Savings on U.S. Petroleum Import s
Based on a detailed analysis of differences in fuel consumption, petroleum imports, and imports of refined petroleum products among the Reference Case, High Economic Growth, and Low Economic Growth Scenarios presented in AEO 2009, NHTSA and EPA estimate that approximately 50 percent of the reduc tion in fuel consumption resulting from adopting improved fuel efficiency standards and GHG standards is likely to be reflected in reduced U.S. imports of refined fuel, while the rem aining 50 percent would be expected to be reflected in reduced domestic fuel refining. s Of this latter figure, 90 percent is anticipated to reduce U.S. imports of crude petroleum for use as a refinery feedstock, while the remaining 10 percent is expected to reduce U.S. domestic prod uction of crude petroleum. t Thus on balance, each gallon of fuel saved as a consequence of improved fuel efficiency standards and GHG standards is anticipated to reduce total U.S. i mports of crude petroleum or refined fuel by 0.95 gallons. u Section 5.3 of the RIA contains a discussion rega rding caveats for the fuel savings estimated due to implementation of this rul e.



### 4.2.9 Air Pollutant Emissions

Car and light truck use, fuel refining, and fuel di stribution and retailing also generate emissions of certain criteria air pollutants, inclu ding carbon monoxide (CO), hydrocarbon compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NO x ), fine particulate matter (PM 2.5 ), and sulfur dioxide (SO 2 ). Emissions of most of these pollutants are associated with the number of vehicl e miles driven, rather than with fuel consumption. While reductions in fuel refining and distribution that result from lower fuel consumption will reduce emissions of criteria pollu tants, additional vehicle use associated with the rebound effect from higher fuel economy wi ll increase emissions of most of these pollutants. Sulfur dioxide is an exception, as des cribed below.

s Differences between forecast annual U.S. imports o f crude petroleum and refined products among these three scenarios range from 24-89% of differences in proje cted annual gasoline and diesel fuel consumption in the U.S. These differences average 49% over the forecast per iod spanned by AEO 2009

.

t Differences between forecast annual U.S. imports o f crude petroleum among these three scenarios range from 67-97% of differences in total U.S. refining of cru de petroleum, and average 85% over the forecast per iod spanned by AEO 2009

.

u This figure is calculated as 0.50 + 0.50*0.9 = 0.5 0 + 0.45 = 0.95. Field Code Changed

Formatted: Font: Not Bold

Formatted: Font: Not Bold

Formatted: Font: Not BoldDraft Joint Technical Support Document

56

Thus the net effect of stricter fuel efficiency and GHG standards on total emissions of each criteria pollutant depends on the relative mag nitudes of reduced emissions in fuel refining and distribution, and increases in emissio ns from vehicle use. Because the relationship between emission rates (emissions per gallon refined of fuel or mile driven) in fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel savings from increased fuel efficiency and GHG stan dards on total emissions of each pollutant is likely to differ.

4.2.9.1 Reductions in Emissions of Criteria Air Pollutants

EPA and NHTSA estimate the increases in emissions o f each criteria air pollutant from additional vehicle use by multiplying the incr ease in total miles driven by cars and light trucks of each model year and age by their estimate d emission rates per vehicle-mile of each pollutant. These emission rates differ between car s and light trucks as well as between gasoline and diesel vehicles, and both their values for new vehicles and the rates at which they increase with age and accumulated mileage can vary among model years. With the exception of SO 2 , the agencies calculated the increase in emissions of these criteria pollutants from added car and light truck use by multiplying the es timated increases in their vehicles’ use during each year over their expected lifetimes by p er-mile emission rates appropriate to each vehicle type, fuel used, model year, and age as of that future year.

These emission rates were estimated by U.S. EPA usi ng its recently-developed Motor Vehicle Emission Simulator (Draft MOVES 2009). The MOVES model assumes that the permile rates at which these pollutants are emitted ar e determined by EPA regulations and the effectiveness of catalytic after-treatment of engin e exhaust emissions, and are thus unaffected by changes in car and light truck fuel economy. As a consequence, the effects of required increases in fuel economy on emissions of these pol lutants from car and light truck use are determined entirely by the increases in driving tha t result from the fuel economy rebound effect.

Emission factors in the MOVES database are expresse d in the form of grams per vehicle-hour of operation. To convert these emissi on factors to grams per mile for use in EPA and NHTSA’s calculations, MOVES was run for the yea r 2050, and was programmed to report aggregate emissions from vehicle starting, o peration, storage, and refueling. EPA analysts selected the year 2050 in order to generat e emission factors that were representative of lifetime average emission rates for vehicles mee ting the agency’s Tier 2 emission standard. v Separate estimates were developed for each vehicl e type and model year, as well as for each state and month, in order to reflect the e ffects of regional and temporal variation in temperature and other relevant variables on emissio ns.

v Because all light-duty emission rates in Draft MOV ES 2009 are assumed to be invariant after MY 2010, a calendar-year 2050 run produced a full set of emission rates that reflect anticipated deterior ation in the effectiveness of vehicles’ emission control systems with increasing age and accumulated mileage for post-MY 2010 vehicles.Economic Assumptions Used in the Agencies’ Analyses

57 The MOVES emissions estimates were then summed to t he model year level and divided by average distance traveled in order to pr oduce per-mile emission factors for each pollutant. The resulting emission rates represent average values across the nation, and incorporate typical temperature variations over an entire calendar year. These national average rates also reflect county-specific differen ces in fuel composition, as well as in the presence and type of vehicle inspection and mainten ance programs. w

Emission rates for the criteria pollutant SO 2 were calculated by EPA and NHTSA using average fuel sulfur content estimates supplie d by EPA, together with the assumption that the entire sulfur content of fuel is emitted i n the form of SO 2 . These calculations assumed that national average gasoline and diesel sulfur le vels would remain at current levels. x There, unlike many other criteria pollutants, sulfur dioxi de emissions from vehicle use decrease proportionally to the decrease in fuel consumption.

Emissions of criteria air pollutants also occur dur ing each phase of fuel production and distribution, including crude oil extraction and tr ansportation, fuel refining, and fuel storage and transportation. The reduction in emissions dur ing each of these phases depends on the extent to which fuel savings result in lower import s of refined fuel, or in reduced domestic fuel refining. To a lesser extent, they also depen d on whether reductions in domestic gasoline refining are reflected in reduced imports of crude oil or in reduced domestic extraction of petroleum.

Both EPA’s and NHTSA’s analyses assume that reducti ons in imports of refined fuel would reduce criteria pollutant emissions during fu el storage and distribution only. Reductions in domestic fuel refining using imported crude oil as a feedstock are assumed to reduce emissions during fuel refining, storage, and distribution, because each of these activities would be reduced. Finally, reduced dome stic fuel refining using domesticallyproduced crude oil is assumed to reduce emissions d uring all four phases of fuel production and distribution. y

EPA estimated the reductions in criteria pollutant emissions from producing and distributing fuel that would occur with alternative fuel efficiency and GHG standards using emission rates obtained from Argonne National Labor atories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) model

. 21 The GREET model provides

w The national mix of fuel types includes county-lev el market shares of conventional and reformulated g asoline, as well as county-level variation in sulfur content , ethanol fractions, and other fuel properties. Inspection/maintenance programs at the county level account for detailed program design elements such as test type, inspection frequency, and program coverage by vehicle type and age.

x These are 30 and 15 parts per million (ppm, measur ed on a mass basis) for gasoline and diesel respect ively, which produces emission rates of 0.17 grams of SO 2 per gallon of gasoline and 0.10 grams per gallon o f diesel.

y In effect, this assumes that the distances crude o il travels to U.S. refineries are approximately the same regardless of whether it travels from domestic oilf ields or import terminals, and that the distances t hat gasoline travels from refineries to retail stations are appr oximately the same as those from import terminals t o gasoline stations.Draft Joint Technical Support Document

58

separate estimates of air pollutant emissions that occur in four phases of fuel production and distribution: crude oil extraction, crude oil trans portation and storage, fuel refining, and fuel distribution and storage. z EPA modified the GREET model to change certain as sumptions about emissions during crude petroleum extraction a nd transportation, as well as to update its emission rates to reflect adopted and pending EPA e mission standards. EPA also incorporated emission factors for the air toxics es timated in this analysis: benzene, 1,3- butadiene, acetaldehyde, acrolein, and formaldehyde . The resulting emission factors are shown in Table 4-13Table 4

- 13 Table 4

- 13

.

z Emissions that occur during vehicle refueling at r etail gasoline stations (primarily evaporative emis sions of volatile organic compounds, or VOCs) are already ac counted for in the “tailpipe” emission factors used to estimate the emissions generated by increased light truck use. GREET estimates emissions in each phas e of gasoline production and distribution in mass per un it of gasoline energy content; these factors are th en converted to mass per gallon of gasoline using the average en ergy content of gasoline. Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

59 Table 4-13 Emissions by Stage of Fuel Production an d Distribution (grams/million Btu)

Pollutant Fuel Type Petroleum Extraction & Transportation 1 Refinery

Energy Use Upstream Emissions Petroleum

Refining On-Site Petroleum

Refining 2 Fuel

Transport, Storage, Distribution 3

CO Conventional Gasoline 4.908 0.928 5.596 6.525 0.748

Reformulated Gasoline 4.908 0.908 5.662 6.571 0.768 Low Sulfur Diesel 4.908 0.800 5.103 5.903 0.780 VOC Conventional Gasoline 3.035 0.602 2.560 3.162 42.91

Reformulated Gasoline 3.035 0.627 2.584 3.211 42.92 Low Sulfur Diesel 3.035 0.552 2.511 3.063 1.261 NOx Conventional Gasoline 14.91 3.328 14.442 17.771 3.691

Reformulated Gasoline 14.91 3.288 14.575 17.863 3.786 Low Sulfur Diesel 14.91 2.895 12.972 15.866 3.570 SOx Conventional Gasoline 3.926 4.398 9.678 14.076 0.886

Reformulated Gasoline 3.926 4.422 9.922 14.344 0.909 Low Sulfur Diesel 3.926 3.893 9.187 13.080 0.840 PM2.5 Conventional Gasoline 0.645 1.442 1.789 3.231 0.155

Reformulated Gasoline 0.645 1.487 1.838 3.325 0.159 Low Sulfur Diesel 0.645 1.309 1.635 2.943 0.133 Air Toxics

1,3-Butadiene Conventional Gasoline 0.0017 0.0003 0.0014 0.0017 0 .0001

Reformulated Gasoline 0.0017 0.0003 0.0014 0.0018 0.0001 Low Sulfur Diesel 0.0017 0.0003 0.0014 0.0017 0.0001 Acetaldehyde Conventional Gasoline 0.0002 0.0000 0.0002 0.0002 0.0046

Reformulated Gasoline 0.0002 0.0000 0.0002 0.0002 0.0047 Low Sulfur Diesel 0.0002 0.0000 0.0002 0.0002 0.0044 Acrolein Conventional Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006

Reformulated Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006 Low Sulfur Diesel 0.0001 0.0000 0.0001 0.0001 0.0006 Benzene Conventional Gasoline 0.0313 0.0062 0.0264 0.0326 0.0787

Reformulated Gasoline 0.0313 0.0064 0.0264 0.0328 0.0788 Low Sulfur Diesel 0.0313 0.0058 0.0264 0.0322 0.0015 Formaldehyde Conventional Gasoline 0.0050 0.0010 0.0042 0.0052 0.0326

Reformulated Gasoline 0.0050 0.0010 0.0042 0.0052 0.0335 Low Sulfur Diesel 0.0050 0.0009 0.0042 0.0051 0.0316

1 The petroleum extraction and transport emission f actors are only applied to domestic crude oil.
2 Refinery emissions factors are applied to domesti c refineries, whether refining domestic or imported crude.
3 Fuel transport, storage, and distribution emissio n factors represent domestic emissions and are appl ied to all finished fuel, whether refined domestically or inte rnationally.
The agency converted these emission rates from the mass per fuel energy content basis on which GREET reports them to mass per gallon of f uel supplied using the estimates of fuel energy content reported by GREET. The resulting em ission rates were applied to both EPA’s and NHTSA’s estimates of fuel consumption under alt ernative fuel efficiency standards to develop estimates of total emissions of each criter ia pollutant during fuel production and distribution. The assumptions about the effects of changes in fuel consumption on domestic and imported sources of fuel supply discussed above were then employed to calculate the Formatted TableDraft Joint Technical Support Document

60

effects of reductions in fuel use from alternative fuel efficiency and GHG standards on changes in domestic emissions of each criteria poll utant.
Finally, EPA and NHTSA calculated the net changes in domestic emissions of each criteria pollutant by summing the increases in its emissions projected to result from increased vehicle use, and the reductions in emissions antici pated to result from lower domestic fuel refining and distribution. aa As indicated previously, the effect of adopting i mproved fuel efficiency and GHG standards on total emissions of each criteria pollutant depends on the relative magnitudes of the resulting reduction in e missions from fuel refining and distribution, and the increase in emissions from additional vehic le use. Although these net changes vary significantly among individual criteria pollutants, we project that on balance, adopting improved fuel efficiency and GHG standards would re duce emissions of all criteria air pollutants except carbon monoxide (CO).

4.2.9.2 Estimated Values of Reducing Criteria Pollutant Emi ssions

In this analysis, EPA and NHTSA estimate the econom ic value of the human health benefits associated with reducing exposure to PM 2.5 . Due to analytical limitations, the estimated values do not include comparable benefits related to reductions in other criteria pollutants (such as ozone, NO 2 or SO 2 ) or toxic air pollutants, nor do they monetize all of the potential health and welfare effects associated wit h PM 2.5 or the other criteria pollutants. As a result these values underestimate the benefits asso ciated with reductions of these pollutants.

This analysis uses a “benefit-per-ton” method to es timate selected PM 2.5 -related health benefits. These PM 2.5 -related benefit-per-ton estimates provide the tota l monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of directly emitted PM 2.5 , or one ton of a pollutant that contributes to sec ondarily-formed PM 2.5 (such as NOx, SOx, and VOCs) from a specified sour ce. Ideally, the human health benefits would be estimated based on changes in amb ient PM 2.5 concentrations and population exposure, as determined by full-scale air quality a nd exposure modeling. However, conducting such detailed modeling was not possible within the timeframe for this proposal.

The dollar-per-ton estimates for reductions in PM 2.5 emissions used in this analysis are provided in Table 4-14Table 4

- 14 Table 4

- 14

.

aa All emissions from increased vehicle use are assum ed to occur within the U.S., since Fuel efficiency standards would apply only to vehicles produced for sale in t he U.S. Field Code ChangedEconomic Assumptions Used in the Agencies’ Analyses

61 Table 4-14 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for PM-related Premature Mortality (Pope et al., 2002) a and a 3% Discount Rate b

All Sources d Stationary (Non-EGU)
Sources e Mobile Sources Year c

SOx VOC NOx Direct PM2.5 NOx Direct PM2.5 2015 $28,000 $1,200 $4,700 $220,000 $4,900 $270,000 2020 $31,000 $1,300 $5,100 $240,000 $5,300 $290,000 2030 $36,000 $1,500 $6,100 $280,000 $6,400 $350,000 2040 $43,000 $1,800 $7,200 $330,000 $7,600 $420,000

a The benefit-per-ton estimates presented in this tab le are based on an estimate of premature mortality derived from the ACS study (Pope et al., 2002). If the ben efit-per-ton estimates were based on the Six Cities study (Laden et al., 2006), the values would be approxima tely 145% (nearly two-and-a-half times) larger.
b The benefit-per-ton estimates presented in this ta ble assume a 3% discount rate in the valuation of p remature mortality to account for a twenty-year segmented ce ssation lag. If a 7% discount rate had been used, the values would be approximately 9% lower.

c Benefit-per-ton values were estimated for the year s 2015, 2020, and 2030. For 2040, EPA and NHTSA extrapolated exponentially based on the growth betw een 2020 and 2030.
d Note that the benefit-per-ton value for SOx is bas ed on the value for Stationary (Non-EGU) sources; n o SOx value was estimated for mobile sources. The benefi t-per-ton value for VOCs was estimated across all s ources.

e Non-EGU denotes stationary sources of emissions ot her than electric generating units.

As Table 4-14Table 4

- 14 Table 4

- 14 indicates, EPA projects that the per-ton values fo r reducing emissions of criteria pollutants from both vehicle use and stationary sources such as fuel refineries and storage facilities will increas e over time. These projected increases reflect rising income levels, which are assumed to increase affected individuals’ willingness to pay for reduced exposure to health threats from air pol lution, as well as population growth into the future, expanding the population exposed to air pol lution in both urban and rural areas.

For certain PM 2.5 -related pollutants (such as direct PM 2.5 and NOx), EPA estimates different per-ton values for reducing mobile sourc e emissions than for reductions in emissions of the same pollutant from stationary sou rces such as fuel refineries and storage facilities. These reflect differences in the typic al geographic distributions of emissions of each pollutant, their contributions to ambient leve ls of PM 2.5 , and resulting changes in population exposure. EPA and NHTSA apply these sep arate values to its estimates of changes in emissions from vehicle use and from fuel production and distribution to determine the net change in total economic damages from emiss ions of those pollutants.

The benefit per-ton technique has been used in prev ious analyses, including EPA’s recent Ozone National Ambient Air Quality Standards (NAAQS) RIA (U.S. EPA, 2008a), 22
Portland Cement National Emissions Standards for Ha zardous Air Pollutants (NESHAP) RIA (U.S. EPA, 2009a), 23 and NO 2 NAAQS (U.S. EPA, 2009b). 24 Table 4-15Table 4

- 15 Field Code Changed

Field Code ChangedDraft Joint Technical Support Document

62

Table 4

- 15 shows the quantified and unquantified PM 2.5 -related co- benefits captured in these benefit-per-ton estimates.

Table 4-15 Human Health and Welfare Effects of PM 2.5
Pollutant / Effect Quantified and Monetized

in Primary Estimates Unquantified Effects Changes in: PM 2.5 Adult premature mortality

Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Visibility Household soiling

Consistent with the NO 2 NAAQS, bb the benefits estimates utilize concentrationresponse functions as reported in the epidemiology literature. Readers interested in reviewing the complete methodology for creating the benefit-p er-ton estimates used in this analysis can consult the Technical Support Document (TSD) 25 accompanying the recent final ozone NAAQS RIA (U.S. EPA, 2008a). Readers can also ref er to Fann et al. (2009) 26 for a detailed description of the benefit-per-ton methodology. cc

As described above, national per-ton estimates were developed for selected pollutant/source category combinations. The per-to n values calculated therefore apply only to tons reduced from those specific pollutant/source c ombinations ( e.g. , NO 2 emitted from mobile sources; direct PM emitted from stationary s ources). Our estimate of total PM 2.5 benefits is therefore based on the total direct PM 2.5 and PM 2.5 –related precursor emissions (NOx, SOx, and VOCs) controlled from each source an d multiplied by the respective per-ton values of reducing emissions from that source.

bb Although we summarize the main issues in this chap ter, we encourage interested readers to see benefits chapter of the NO2 NAAQS for a more detail ed description of recent changes to the PM benefits presentation and preference for the no-threshold mo del.

cc The values included in this report are different f rom those presented in the article cited above. Be nefits methods change to reflect new information and evalu ation of the science. Since publication of the Jun e 2009 article, EPA has made two significant changes to it s benefits methods: (1) We no longer assume that a threshold exists in PM-related models of health impacts; and (2) We have revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an estimate of $ 5.5 million (year 2000$) used in the June 2009 repo rt. Please refer to the following website for updates to the d ollar-per-ton estimates: http://www.epa.gov/air/benmap/bpt.htmlEconomic Assumptions Used in the Agencies’ Analyses

63 The benefit-per-ton coefficients in this analysis w ere derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit-per-to n estimates first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which incorpor ated concentration-response functions directly from the epidemiology studies, w ithout any adjustment for an assumed threshold. Removing the threshold assumption is a key difference between the method used in this analysis to estimate PM co-benefits and the methods used in analyses prior to EPA’s Portland Cement NESHAP. As a consequence, the bene fit-per-ton estimates used in this analysis include incremental benefits of reductions in PM 2.5 concentrations down to their lowest modeled levels.

Reductions in PM-related mortality provide the majo rity of the monetized value in each benefit-per-ton estimate. Typically, the prem ature mortality-related effect coefficients that underlie the benefits-per-ton estimates are dr awn from epidemiology studies that examine two large population cohorts: the American Cancer S ociety cohort (Pope et al., 2002) 27 and the Harvard Six Cities cohort (Laden et al., 2006). 28 The concentration-response (C-R) function developed from the extended analysis of Am erican Cancer Society (ACS) cohort, as reported in Pope et al. (2002), has previously been used by EPA to generate its primary benefits estimate. The extended analysis of the Ha rvard Six Cities cohort, as reported by Laden et al (2006), was published after the complet ion of the Staff Paper for the 2006 PM 2.5 NAAQS and has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 cobenefits estimates in analyses completed since the PM 2.5 NAAQS.

These studies provide logical choices for anchor po ints when presenting PM-related benefits because, while both studies are well desig ned and peer-reviewed, there are strengths and weaknesses inherent in each. Although this arg ues for using both studies to generate benefits estimates, due to the analytical limitatio ns associated with this analysis, dd EPA and NHTSA have chosen to use the benefit-per-ton value derived from the ACS study. The agencies note that benefits would be approximately 145 percent (or nearly two-and-a-half times) larger if the per-ton benefit values based o n the Harvard Six Cities were used instead.

As is the nature of benefits analyses, assumptions and methods evolve over time to reflect the most current interpretation of the scie ntific and economic literature. For a period of time (2004-2008), EPA’s Office of Air and Radiat ion (OAR) valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a VS L using a range of $1 million to $10 million (2000$) consistent with two meta-analyses o f the wage-risk literature.
The $1 million value represented the lower end of t he interquartile range from the Mrozek and Taylor (2002) 29 meta-analysis of 33 studies. The $10 million valu e represented the upper end of the interquartile range from the V iscusi and Aldy (2003) 30 meta-analysis of 43 studies. The mean estimate of $5.5 million (200 0$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. ( 2006) 31 meta-analysis. However, the

dd Add note about VECTOR/VOLPE input li mitationsDraft Joint Technical Support Document

64

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected the interim estimate to a scientific peer-review pr ocess through the Science Advisory Board (SAB) or other peer-review group.

Until updated guidance is available, EPA determined that a single, peer-reviewed estimate applied consistently best reflects the SAB -EEAC advice it has received. Therefore, EPA has decided to apply the VSL that was vetted an d endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 32 while they continue efforts to update their guidance on this issue. ee This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuati on studies published between 1974 and 1991. The mean VSL across these studies is $6.3 mi llion (2000$). The dollar-per-ton estimates used in this analysis are based on this r evised VSL.

The benefit-per-ton estimates are subject to a numb er of assumptions and uncertainties.

a. They do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local fac tors that might lead to an overestimate or underestimate of the actual benefit s of controlling fine particulates in specific locations. EPA will conduct full-scale ai r quality modeling for the final rulemaking in an effort to capture this variability . Please refer to Chapter 7.2 of the DRIA for a description of EPA’s modeling plans and to Chapter 7.3 for the description of the agency’s plans for quantification and moneti zation of health impacts for the FRM.

a. b. This analysis assumes that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from st ationary sources may differ significantly from direct PM 2.5 released from diesel engines and other industrial sources. At the present time, however, no clear scientific grounds exist for supporting differential effects estimates by partic le type.

a. c. This analysis assumes that the health impact functi on for fine particles is linear within the range of ambient concentrations under considera tion. Thus, the estimates include health benefits from reducing fine particles in are as with varied initial concentrations of PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard, down to the lo west modeled concentrations.

a. d. There are several health benefits categories that E PA and NHTSA were unable to quantify due to limitations associated with using b enefits-per-ton estimates, several of which could be substantial. Because the NO X and VOC emission reductions associated with this proposal are also precursors t o ozone, reductions in NO X and VOC

ee In the (draft) update of the Economic Guidelines ( U.S. EPA, 2008c), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be fort hcoming in the near future. Therefore, this report does no t represent final agency policy. The draft update of the Economic Guidelines is available on the Internet at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/ EE0516-01.pdf/$File/EE-0516-01.pdf>. Formatted: Bullets and NumberingEconomic Assumptions Used in the Agencies’ Analyses

65 would also reduce ozone formation and the health ef fects associated with ozone exposure. Unfortunately, benefits-per-ton estimate s for ozone do not exist due to issues associated with the complexity of the atmosp heric air chemistry and nonlinearities associated with ozone formation. Th e PM-related benefits-per-ton estimates also do not include any human welfare or ecological benefits. Please refer to Chapter 7.3 of the DRIA for a description of the qu antification and monetization of health impact for the FRM and a description of the unquantified co-pollutant benefits associated with this rulemaking.

As mentioned above, emissions changes and benefits- per-ton estimates alone are not a good indication of local or regional air quality an d health impacts, as the localized impacts associated with the proposed rulemaking may vary si gnificantly. Additionally, the atmospheric chemistry related to ambient concentrat ions of PM 2.5 , ozone and air toxics is very complex. Full-scale photochemical modeling is ther efore necessary to provide the needed spatial and temporal detail to more completely and accurately estimate the changes in ambient levels of these pollutants and their associated hea lth and welfare impacts. As discussed above, timing and resource constraints precluded EP A and NHTSA from conducting a fullscale photochemical air quality modeling analysis i n time to include in the NPRM. For the final rule, however, a national-scale air quality m odeling analysis will be performed to analyze the impacts of the standards on PM 2.5 , ozone, and selected air toxics.

4.2.10 

### Reductions in Emissions of Greenhouse Gases

Emissions of carbon dioxide and other greenhouse ga ses (GHGs) occur throughout the process of producing and distributing transportatio n fuels, as well as from fuel combustion itself. By reducing the volume of fuel consumed by passenger cars and light trucks, higher fuel efficiency standards will thus reduce GHG emis sions generated by fuel use, as well as throughout the fuel supply cycle. Lowering these e missions is likely to slow the projected pace and reduce the ultimate extent of future chang es in the global climate, thus reducing future economic damages that changes in the global climate are otherwise expected to cause. Further, by reducing the probability that climate c hanges with potentially catastrophic economic or environmental impacts will occur, lower ing GHG emissions may also result in economic benefits that exceed the resulting reducti on in the expected future economic costs caused by gradual changes in the earth’s climatic s ystems. Quantifying and monetizing benefits from reducing GHG emissions is thus an imp ortant step in estimating the total economic benefits likely to result from establishin g improved fuel efficiency and GHG standards.

4.2.10.1 Estimating Reductions in GHG Emissions

The agencies estimated emissions of carbon dioxide (CO 2 ) from passenger car and light truck use by multiplying the number of gallon s of each type of fuel (gasoline and diesel) they are projected to consume with each alternative CAFE standard in effect by the quantity or mass of CO 2 emissions released per gallon of fuel consumed. T his calculation assumes that the entire carbon content of each fuel is ulti mately converted to CO 2 emissions during the combustion process. The weighted average CO 2 content of gasoline is estimated to be approximately 8,900 grams per gallon, while that of diesel fuel is estimated to beDraft Joint Technical Support Document

66

approximately 10,200 grams per gallon. For details , please see EPA’s DRIA and NHTSA’s PRIA.

Although carbon dioxide emissions account for nearl y 95 percent of total GHG emissions that result from fuel combustion during v ehicle use, emissions of other GHGs are potentially significant as well because of their hi gher “potency” as GHGs than that of CO 2 itself. EPA and NHTSA estimated the increases in e missions of methane (CH 4 ) and nitrous oxide (N 2 O) from additional vehicle use by multiplying the i ncrease in total miles driven by cars and light trucks of each model year and age by emission rates per vehicle-mile for these GHGs. These emission rates, which differ between c ars and light trucks as well as between gasoline and diesel vehicles, were estimated by EPA using its recently-developed Motor Vehicle Emission Simulator (Draft MOVES 2009), as s ubsequently updated for use in this analysis.

The MOVES model assumes that the per-mile rates at which cars and light trucks emit these GHGs are determined by the efficiency of fuel combustion during engine operation and chemical reactions that occur during catalytic afte r-treatment of engine exhaust, and are thus independent of vehicles’ fuel consumption rates. T hus MOVES emission factors for these GHGs, which are expressed per mile of vehicle trave l, are assumed to be unaffected by changes in fuel economy.

Increases in emissions of non-CO 2 GHGs are converted to equivalent increases in CO 2 emissions s u s ing estimates of the Global Warming Potential (GWP) of methane and nitrous oxide. These GWPs account for the higher radiative forcing capacity of methane and nitrous oxide when they are released into the earth’s atmos phere, measured relative to that of CO 2 itself. The CO 2 equivalents of increases in emissions of these gas es are then added to the increases in emissions of CO 2 itself to determine the effect of the total increa se in CO 2 - equivalent GHG emissions from vehicle use.

4.2.10.2 Economic Benefits from Reducing GHG Emissions

EPA and NHTSA assigned a dollar value to reductions in CO 2 emissions using the marginal dollar value ( i.e. , cost) of climate-related damages resulting from c arbon emissions, also referred to as “social cost of carbon” (SCC). The SCC is intended to measure the monetary value society places on impacts resulting from increased GHGs, such as property damage from sea level rise, forced migration due to dry land loss, and mortality changes associated with vector-borne diseases. Published e stimates of the SCC vary widely as a result of uncertainties about future economic growth, clim ate sensitivity to GHG emissions, procedures used to model the economic impacts of cl imate change, and the choice of discount rates. EPA and NHTSA’s coordinated propos als present a set of interim SCC values reflecting a federal interagency group’s interpreta tion of the relevant climate economics literature. Sections III.H.6 and IV.C.3.l of the p reamble provide more detail about SCC.



### 4.2.11 The Value of Increased Driving Range

Improving the fuel economy of passenger cars and li ght-duty trucks may also increase their driving range before they require refueling. By reducing the frequency with whichEconomic Assumptions Used in the Agencies’ Analyses

67 drivers typically refuel their vehicles and extendi ng the upper limit of the range they can travel before requiring refueling, improving fuel e conomy thus provides some additional benefits to their owners. Alternatively, if manufa cturers respond to improved fuel economy by reducing the size of fuel tanks to maintain a co nstant driving range, the resulting cost saving will presumably be reflected in lower vehicl e sales prices. If manufacturers respond by doing so, this presumably reflects their judgmen t that the value to economic benefits to vehicle buyers from lower purchase prices exceeds t hat from extended refueling range.

No direct estimates of the value of extended vehicl e range are readily available, so the agencies’ analyses calculate the reduction in the a nnual number of required refueling cycles that results from improved fuel economy, and applie s DOT-recommended values of travel time savings to convert the resulting time savings to their economic value. 33 As a n coarse illustration of how the value of extended refueling range is estimated, a typical small light truck model has an average fuel tank size of approx imately 20 20 gallons. ff Based on a California Air Resources Board Study, the average fill fuel purchase is approximately 55% of tank volume. 34 Assuming that drivers typically refuel when their t a nks are 20 percent full Therefore, ( i.e. , 4 gallons in reserve), increasing this model’s actual on-road fuel economy from 24 to 25 mpg would extend its driving range fr om 384 216 miles (= 9 16 gallons x 24 mpg) to 400 225 miles (= 16 9 gallons x 25 mpg). Assuming that this vehicle is d riven 12,000 miles/year, this reduces the number of times it needs to be refueled each year from 31.3 55.5 (= 12,000 miles per year / 384 216 miles per refueling) to 30.0 53.3 (= 12,000 miles per year / 400 225 miles per refueling), or by 1.3 2.2 refuelings per year.

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and average vehicle occupancy for all driving trips (1. 6 persons), the DOT-recommended value of travel time per vehicle-hour is $24.00 (in 2006 dol lars). gg Assuming that locating a station and filling up requires ten five minutes, the annual value of time saved as a result of less frequent refueling amounts to $ 2

. 6 0 4.00 (calculated as 5/60 x 2 1

. 3 2 x $24.00). This calculation is repeated for each future calendar year that light t rucks of each model year affected by the alternative fuel economy standards considered in th is rule would remain in service. Like fuel savings and other benefits, however, the value of t his benefit declines over a model year’s lifetime, because a smaller number of vehicles orig inally produced during that model year remain in service each year, and those remaining in service are driven fewer miles. The detailed values used in this calculation by each ag ency are available in EPA’s DRIA and NHTSA’s PRIA respectively.

ff Based on the Volpe Model Market Data file for Mode l Year 2011, average tank volumes for cars and truc ks are 16.6 gallons and 23.0 gallons, respectively. This produces a production weighted average of 19.3 gall ons.

gg The hourly wage rate during 2006 is estimated to be $24.00. Personal travel (94.4% of urban travel) is valued at 50 percent of the hourly wage rate. Business tra vel (5.6% or urban travel) is valued at 100 percent of the hourly wage rate. For intercity travel, personal t ravel (87%) is valued at 70 percent of the wage rat e, while business travel (13%) is valued at 100 percent of t he wage rate. The resulting values of travel time are $12.67 for urban travel and $17.66 for intercity travel, a nd must be multiplied by vehicle occupancy (1.6) to obtain the estimate value of time per vehicle hour. Formatted: Indent: First line: 0"Draft Joint Technical Support Document

68

The agencies’ estimate of benefits from less freque nt refueling is subject to several sources of uncertainty.

First, this analysis assumes that manufacturers wi ll not adjust fuel tank capacities downward (from the current average of 19.3 gallons) when they improve the fuel economy of their vehicle models, so that the entire increase i n fuel economy will be reflected in increased driving range. Should manufacturers choose to downs ize fuel tanks, vehicle space, utility and value will increase and vehicle weight will decreas e, improving fuel economy and CO 2 emissions (all other things being equal). In the c ontext of the rule, this will decrease the cost of compliance with the proposed standards

. Second, the agencies’ analysis assumes that fuel purchases on average , a refill is 55% of fuel tank capacity. However, as shown in the Ca lifornia Air Resource Board (CARB) report, refueling patterns vary. Moreover, the 55% estimate implies that drivers, on average, are either refueling with when nearly a half tank of gas remains in their vehicles, or that they are habitually not filling their tanks. Since many drivers only refuel when their tanks are very low, and since many drivers habitually fill up thei r tanks, this in turn implies that many drivers in the CARB study are refueling with when their tanks are still well above 50% full. Certainly instances of this type of behavior occur, but the CARB study implies they are that it is the norm. Behavior that maximizes the number of f ill-ups implies a very low value of time, but it is also possible that the results of t he CARB study are not representative of typical behavior across the country. While based on field data, this estimate may thus overestimate the impact of refueling benefits.

Third, the agencies’ estimate of refueling benefits assumes that refueling stops involve the same number of vehicle occupants as the overall average for all vehicle trips (1.6 persons). To the extent that drivers refuel while doing other errands or in advance of picking up passengers, this figure may overestimate the typica l vehicle occupancy during refueling, and thus the total savings in refueling time. Similarl y, the hourly value used to estimate the economic value of savings in refueling time reflect s the typical mix of personal and business travel purposes, and drivers are likely to assign d ifferent values to their time when traveling for these different purposes. To the extent that d rivers seek to refuel when traveling for purposes that typically use less valuable time, the hourly value used in the agencies’ analysis may overstate the benefits from saving refueling ti me.
Finally, the agencies assume that both finding and using a refueling station takes, on average, five minutes. There are few, if any, data sources on average refueling time, and this estimate is subject to significant uncertainty.

For these reasons, the agencies’ estimate of saving s in refueling time is uncertain. As noted in section II of the preamble, the agencies s eek comment or data on of each of the assumptions they use to estimate this benefit, incl uding alternative empirical estimates of the parameters used in its calculations.Economic Assumptions Used in the Agencies’ Analyses

69 

### 4.2.12 Discounting Future Benefits and Costs

Discounting future fuel savings and other benefits is intended to account for the reduction in their value to society when they are d eferred until some future date, rather than received immediately. The discount rate expresses the percent decline in the value of these benefits – as viewed from current perspective – for each year they are deferred into the future. In evaluating the benefits from alternative increas es in fuel economy and GHG standards for MY 2012-2016 passenger car sand light trucks, EPA a nd NHTSA have employed a discount rate of 3 percent per year.

The primary reason that the agencies have selected 3 percent as the appropriate rate for discounting future benefits from increased fuel economy and GHG standards is that most or all of vehicle manufacturers’ costs for complyin g with improved fuel economy and GHG standards are likely to be reflected in higher sale s prices for their new vehicle models. By increasing sales prices for new cars and light truc ks, CAFE regulation will thus primarily affect vehicle purchases and other private consumpt ion decisions.

Both economic theory and OMB guidance on discountin g indicate that the future benefits and costs of regulations that mainly affec t private consumption should be discounted at the consumption rate of time preference. 35 OMB guidance indicates that savers appear to discount future consumption at an average real (tha t is, adjusted to remove the effect of inflation) rate of about 3 percent when they face l ittle risk about its likely level, which makes it a reasonable estimate of the consumption rate of time preference. 36 Thus EPA and NHTSA have employed the 3 percent rate to discount projec ted future benefits and costs resulting from improved fuel economy and GHG standards for MY 2012-2016 passenger cars and light trucks.

Because there is some uncertainty about the extent to which vehicle manufacturers will be able to recover their costs for complying w ith improved fuel economy and GHG standards by increasing vehicle sales prices, howev er, the agencies have also tested the sensitivity of these benefit and cost estimates to the use of a higher percent discount rate. OMB guidance indicates that the real economy-wide o pportunity cost of capital is the appropriate discount rate to apply to future benefi ts and costs when the primary effect of a regulation is “...to displace or alter the use of cap ital in the private sector,” and estimates that this rate currently averages about 7 percent. 37 Thus the agencies have also tested the sensitivity of its benefit and cost estimates for a lternative MY 2012-2016 fuel economy and GHG standards to the use of a 7 percent real discou nt rate.Draft Joint Technical Support Document

70 Reference

1 EPA, Fuel Economy Labeling of Motor Vehicles: Revi sions To Improve Calculation of Fuel Economy Estimates; Final Rule, 40 CFR Parts 86 and 600, Fed eral Register, December 27, 2006, pp. 77872-77969, http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/D ay-27/a9749.pdf

.
2 EPA, Final Technical Support Document: Fuel Economy Labe ling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates , Office of Transportation and Air Quality EPA420-R -06-017 December 2006, Chapter II, http://www.epa.gov/fueleconomy/420r06017.pdf

.
3 Energy Information Administration, Annual Energy O utlook 2009, Revised Updated Reference Case (April 2009), Table 12. Available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/exc el/aeostimtab_12.xls (last accessed July 26, 2009). EIA’s Updated Reference Case reflects the effects of the American Reinvestm ent and Recovery Act of 2009, as well as the most recent re visions to the U.S. and global economic outlook.

4 http://www.eia.doe.gov/oiaf/aeo/forecast.html (las t accessed July 26, 2009), and Table 16, http://www.eia.doe.gov/oiaf/aeo/pdf/table16.pdf (la st accessed July 26, 2009).

5 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed April 20, 2008). These updated survival rates suggest that the expected lifetimes of recent-model passenger cars a nd light trucks are 13.8 and 14.5 years.

6 FHWA, Highway Statistics, Summary to 1995, Table v m201at http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw , and annual editions 1996-2005, Table VM-1 at http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm (last accessed April 20, 2008).

7 Sorrell, S. and J. Dimitropoulos, 2007. “UKERC Rev iew of Evidence for the Rebound Effect, Technical R eport 2: Econometric Studies”, UKERC/WP/TPA/2007/010, UK Energy Research Centre, London, October and Greening, L.A., D.L. Greene and C. Difiglio, 2000. “Energy Efficiency and Consumption – The Rebound Ef fect – A Survey”, Energy Policy, vol. 28, pp. 389-401.

8 Greening et al. 2000.

9 Pickrell, D. and P. Schimek, 1999. “Growth in Moto r Vehicle Ownership and Use: Evidence from the Nationwide Personal Transportation Survey”, Journal of Transportation and Statistics, vol. 2, no. 1, p p. 1-17.

10 Small, K. and K. Van Dender, 2007a. “Fuel Efficien cy and Motor Vehicle Travel: The Declining Rebound Effect”, The Energy Journal, vol. 28, no. 1, pp. 25 -51.

11 Small, K. and K. Van Dender, 2007b. "Long Run Tren ds in Transport Demand, Fuel Price Elasticities and Implications of the Oil Outlook for Transport Polic y," OECD/ITF Joint Transport Research Centre Discus sion Papers 2007/16, OECD, International Transport Forum

.
12 Greene Report to EPA, March 24, 2009. Note, this re port has been submitted for peer review. Completio n of the peer review process is expected prior to the fi nal rule. See DOCKET NUMBER XX

13 Small Report to EPA, June 12, 2009. INSERT DOCKET NUMBER.

14 FHWA. 1997 Federal Highway Cost Allocation Study ; http://www.fhwa.dot.gov/policy/hcas/final/index.h tm (last accessed July 29, 2009).

15 Federal Highway Administration, 1997 Federal Highway Cost Allocation Study

, http://www.fhwa.dot.gov/policy/hcas/final/index.htm , Tables V-22, V-23, and V-24 (last accessed July 2 7, 2009).

16 U.S. Bureau of Economic Analysis, U.S. Internation al Transactions Accounts Data, as shown on June 24, 2009.

17 U.S. Department of Energy, Annual Energy Review 20 08, Report No. DOE/EIA-0384(2008), Tables 5.1 and 5.13c, June 26, 2009.Economic Assumptions Used in the Agencies’ Analyses

71 18 Leiby, Paul N. "Estimating the Energy Security Ben efits of Reduced U.S. Oil Imports," Oak Ridge Natio nal Laboratory, ORNL/TM-2007/028, Final Report, 2008.

19 Peer Review Report Summary: Estimating the Energy S ecurity Benefits of Reduced U.S. Oil Imports , ICF, Inc., September 2007.

20 Gately, Dermot 2004. "OPEC's Incentives for Faste r Output Growth," The Energy Journal, 25(2):75-96, "What Oil Export Levels Should We Expect From OPEC? " The Energy Journal, 28(2):151-173, 2007

21 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Tra nsportation (GREET) Model , Version 1.8, June 2007, available at http://www.transportation.anl.gov/software/GREET/in dex.html (last accessed April 20, 2008).

22 U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level Ozon e, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Ava ilable at <http://www.epa.gov/ttn/ecas/regdata/RIAs /6- ozoneriachapter6.pdf>.

23 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emissi on Standards for Hazardous Air Pollutants from the Por tland Cement Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle P ark, NC. April. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandc ementria_4-20-09.pdf>.

24 U.S. Environmental Protection Agency (U.S. EPA). 2009b. Proposed NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Av ailable on the Internet at http://www.epa.gov/ttn/ecas/regd ata/RIAs/proposedno2ria.pdf.

25 U.S. Environmental Protection Agency (U.S. EPA). 2008b. Technical Support Document: Calculating Bene fit Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2 007-0225-0284. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available on the Internet at <http://www.regulatio ns.gov>.

26 Fann, N. et al. (2009). The influence of location , source, and emission type in estimates of the hum an health benefits of reducing a ton of air pollution. Air Q ual Atmos Health. Published online: 09 June, 2009.

27 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Cal le, D. Krewski, K. Ito, and G.D. Thurston. 2002. “ Lung Cancer, Cardiopulmonary Mortality, and Long-term Ex posure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

28 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Doc kery. 2006. “Reduction in Fine Particulate Air Pol lution and Mortality.” American Journal of Respiratory an d Critical Care Medicine 173:667-672. Estimating t he Public Health Benefits of Proposed Air Pollution Re gulations. Washington, DC: The National Academies Press.

29 Mrozek, J.R., and L.O. Taylor. 2002. “What Deter mines the Value of Life? A Meta-Analysis.” Journa l of Policy Analysis and Management 21(2):253-270.

30 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Es timates throughout the World.” Journal of Risk and Uncerta inty 27(1):5-76.

31 Kochi, I., B. Hubbell, and R. Kramer. 2006. An E mpirical Bayes Approach to Combining Estimates of t he Value of Statistical Life for Environmental Policy Analysis. Environmental and Resource Economics. 3 4: 385- 406.

32 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Center for Environmenta l Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/cover.pdf>.

33 See http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf and http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2- 11-03.pdfDraft Joint Technical Support Document

72 34 California Environmental Protection Agency, Air Re sources Board. Draft Assessment of the Real-World Impacts of Commingling California Phase 3 Reformula ted Gasoline. August 2003

35 Id.

36 Office of Management and Budget, Circular A-4, “Re gulatory Analysis,” September 17, 2003, 33. Available

at http://www.whitehouse.gov/omb/circulars/a004/a-4.p df (last accessed July 24, 2009).

37 Id.